INCORPORATING MODIS LAND SURFACE TEMPERATURE IN AN OPERATIONAL SNOW ACCUMULATION AND ABLATION MODEL

Transcription

1 INCORPORATING MODIS LAND SURFACE TEMPERATURE IN AN OPERATIONAL SNOW ACCUMULATION AND ABLATION MODEL EYLON SHAMIR AND KONSTANTINE P. GEORGAKAKOS HRC TECHNICAL NOTE NO. 55 (Sponsored by NASA Grant No: NNX12AQ37G) HYDROLOGIC RESEARCH CENTER High Bluff Drive, Suite 255, San Diego, CA 9213, USA 1 September 213 1

2 CONTENTS Acknowledgments... 3 Executive Summary Introduction Motivation Literature Review Data and Methods Study Region: In situ observations MODIS LST Data for the Study Area LST Product Availability LST Correspondence with Air Surface Temperature Snow 17 Model Surface Temperature Interpolation Filling of Missing LST Values Air Surface Interpolation from MODIS LST Air Surface Interpolation from Gauges Snow Pack Impacts Summary, Conclusions and Extensions Summary Conclusions Extensions References Appendix A: Monthly Intercomparion of Estimated Surface Air Temperature Appendix B: Association between Ta and LST for gauge sites in Turkey 44 2

3 ACKNOWLEDGMENTS The work reported herein was supported by the NASA Grant No. NNX12AQ27G. We would like to thank Ayhan Sayin and Hayrettin Bacanli from the Turkish State Meteorological Service for providing the hydrometeorological data. We are grateful to Christa Peters-Lidard of the Hydrological Sciences Laboratory, Goddard Space Flight Center, NASA, for her valuable suggestions and information during the tenure of this work. 3

4 EXECUTIVE SUMMARY Regional operational modeling systems that support forecasters for real-time warning of flash flood events are being used in many regions worldwide. These systems provide hydrological and meteorological agencies with a tool to evaluate and produce timely alerts, watches and warnings for flash floods in small basins (area of order of 1 km 2 ). The output of the operational modeling systems is a product that estimates the amount of rainfall of a given duration that is required to cause minor flooding at the outlet of small flash-flood prone basins (hereinafter, flash flood guidance or FFG). These FFG indices are dynamically updated, adjusted by forecasters using locally available data, and then compared with various rainfall nowcast products to develop and disseminate prompt warnings and alerts for flash floods. In many regions the values of the FFG are highly dependent on the extent and depth of the seasonal snowpack in the basins. The snowpack impact on the FFG values and on the estimated risk for a flood at the basin outlet is through the determination of the fractions of the nowcast precipitation that falls on snow-free ground and on snowpack. Estimates of FFG also require accurate estimates of the underlying soil water that is influenced by the accumulation and ablation of the snowpack. In the regional operational systems the snowpack and melt properties are being dynamically estimated by the Snow-17 model which is an energy and mass balance snow accumulation and ablation model that is forced by basin mean areal precipitation and surface air temperature. The Snow-17 model uses air surface temperature as an index for energy exchange between the snow pack and the atmosphere and for the estimation of various energy fluxes. Because of the remote areas covered and the regional scale of these operational flash flood guidance systems, large regions often suffer from lack of real-time data that can be adequately scaled and used as forcing to the snow model. This lack of data is even more acute in remote and inaccessible mountainous regions in which seasonal snowpack is prevalent. The land surface 4

5 temperature (LST) from MODIS which provides four instantaneous readings per day is tested herein as an promising product that can potentially be used in real-time to derive spatially distributed temperature forcing for the snow model. In this study we evaluated the availability and usability of the MODIS LST in the Southeast area of Turkey, a region with high mountains that includes the headwaters of the Euphrates and Tiger Rivers. Hourly air surface temperature, daily snow depth and SWE and daily rainfall datasets were received from the Turkish State Meteorological Services and processed for October 22 - September 21. A comparison between the air surface temperature and the corresponding LST grid-cells indicate tight associations that are different in nature for periods with and without snow. During winter, the LST product was missing about 6% of the time mainly because of cloud masking. The frequency of LST availability of valid readings is improved during spring and summer. We compared the LST-based estimation of surface air temperature to that of interpolation procedures that are based on in-situ air surface air temperature measurements. These interpolation procedures include one that used all available gauge data from 31 stations (atypically dense network for operational conditions), and a procedure that is based on only 1 gauge (more typical for operational network densities). In addition, the interpolation procedures were compared with climatological time series derived from analysis of the gauges, and a 1- minute gridded climatological monthly dataset available from the Climate Research Unit, East Anglia. In general, the LST derived Ta was found inferior only to the Ta derived from the interpolation of the 31 gauges. The effect of the various derived temperature time series on the simulation of the snow pack was examined by comparing the snow simulations using the LSTderived and interpolation estimates of surface air temperature to the snow pack simulation forced by the observed air surface temperature time series for each of 18 gauge sites. The LST derived Ta performed well as model forcing to simulate indices that represent characteristics of snow mass and magnitude. The present assessment indicates that the MODIS LST product can be incorporated into the operational flash flood guidance systems in combination with gauge data to derive real-time 5

6 basin mean areal air surface temperature to be used as forcing for the snow model component. Such implementation is expected improve snowpack simulation in data scarce regions and will improve the product reliability of the system during the spring/summer snow melt periods. 1. INTRODUCTION 1.1 MOTIVATION Snow accumulation and ablation models that track the snowpack seasonal evolution of the energy and mass balance have been applied routinely worldwide to evaluate the snowpack and melt characteristics. In operational setups which require real-time measurements to represent watershed scale snowpack characteristics, air surface temperature is often a key observed variable that serves as an index for a range of energy fluxes in the atmosphere-snow pack interface and the internal snowpack (e.g. Anderson, 1973 and 26). A major uncertainty source in the output of these snow models stems from commonly insufficient density of in-situ meteorological observation network that is required to derive reliable and accurate estimates of energy fluxes and their spatial variability. In mountainous regions with complex terrain and few gauges this uncertainty is expected to be larger (e.g., Bales et al., 26). In addition, in complex terrain the use of a constant to describe the lapse rate adds uncertainty because in these regions the lapse rate varies considerably, being dependent on the synoptic conditions (e.g. Lundquist and Cayan 27). Snow model sensitivity to air surface temperature (Ta) is well demonstrated in Figure 1. The upper panel of Figure 1 compares simulations of snow water equivalent (SWE) for the site of a selected meteorological station in Southeastern Turkey using observed and interpolated surface temperature (Figure 1, lower panel) from a relatively (and atypically) dense observation network. (The details about the snow model, surface temperature dataset and the interpolation procedure will be further discussed in following sections.) Clearly, these two surface temperature time series yielded a substantial difference between the simulations of the snowpack s magnitude denoted as Observed and Interpolated during the winter accumulation and spring ablation time. 6

7 This uncertainty associated with the interpolation of point temperature data is added to the uncertainty infused due to the representation of the energy fluxes as functions of air surface temperature. An example of this uncertainty can be demonstrated by Kuhn (1987). Using a theoretical experiment they showed that during clear calm days when solar radiation is the dominant flux snow melt can occur at air temperature of -1 o C. On the other hand, during clear nights when outgoing longwave radiation is the dominant flux, no melt occurred for air temperature well above o C and the snowpack remained frozen even at 1 o C Observed Ta Interpolated Ta Snow Water Equivalent (mm) Dec 15-Dec 1-Jan 15-Jan 1-Feb 15-Feb 1-Mar 15-Mar 1-Apr 15-Apr 1 T air ( o C) Dec 15-Dec 1-Jan 15-Jan 1-Feb 15-Feb 1-Mar 15-Mar 1-Apr 15-Apr 6-Hour Interval Figure 1: SWE simulation using observed (solid black) and interpolated (dashed gray) air temperature for Gauge #1792 (25-26). The interpolation was conducted using a network of 31 gauges. Lower panel shows the observed (solid black) and interpolated (dashed gray) surface air temperature. 7

8 The simulation of the seasonal evolution of the snowpack and its melt is an important component of operational regional Flash Flood Guidance Systems (FFGS) (Georgakakos, et al. 213). For instance, large areas that are covered by the Black Sea and Middle East (BSMEFFG) system experience winter snow pack with large inter- and intra-annual variability. The seasonal snow pack substantially affects the occurrences of flash floods by modulating the extent of runoff resulting from rainfall on bare ground and from snowmelt. These processes are currently accounted for in the FFG system by an implementation of snow accumulation and ablation model that routinely updates the mass and energy states of the snow pack. The snow model is forced in real-time by hourly precipitation estimates from geostationary satellites (i.e. Hydro- Estimator algorithm from NESDIS/NOAA) and surface temperature as an index for energy fluxes that is interpolated from a network of in-situ gauges. As discussed above and will be further demonstrated, these air surface temperature reports are often scarce and insufficient for snow simulation on a regional scale. Improvement of the snowpack description in the FFGS modeling system is expected to improve flash flood warnings mainly during the transitional seasons (autumn and spring) in which the temporal properties of the snowpack are highly variable. In the autumn and spring seasons there is large variability in storm characteristics and the precipitation often alternates between snow and rain. In addition, during the inception of the winter snowpack and the melt season in spring and early summer there is large variability in the snow extent due to mesoscale synoptic conditions that impact the rate of snow ablation In this study we explore the applicability of the MODIS LST product as a source of information for the snow model. Our motivation to use the MODIS/LST product as a proxy for surface air temperature is driven by its potential to resolve the fine scale features of variability which are commonly not inferable from in-situ gauge networks in complex terrain. Toward this end, a feasibility study was conducted in Southeast Turkey and the present report summarizes relevant findings. In Section 2, following literature review, introduction of the study area, data, and procedures, we discuss and evaluate the association between the LST and observed air surface temperature. In Section 3, the availability of valid LST reports for the study region is evaluated, and a comparison between surface air temperature derived from the LST product and other 8

9 commonly available time series is presented. Last in Section 4, we compare the effect of the interpolated Ta on the simulation of snow pack. Study conclusions and proposed extensions are provided in Section LITERATURE REVIEW Details on the LST algorithm, its day-night split window regression, and its theoretical basis can be found in Wan and Dozier (1996), Wan and Li (1997), Wan (23), and Wan (28). The MODIS LST product can be made operationally available in near real-time, within hours of the satellite overpass, from the MODIS Rapid Response System (Pinheiro, et al. 27). Land Surface Temperature (LST) is an instantaneous measurement of the skin temperature that is mapped from the radiometric (kinetic) temperature and derived from the thermal IR radiation emitted from the land surface. Sensible heat fluxes emitted from the earth surfaces are detected by the MODIS satellite and are used to estimate instantaneous LST. LST algorithm depends on the MODIS brightness temperature bands (11 and 12 µm channels 31 and 32) and surface emissivity estimates for these two channels. The emissivity in the thermal infrared is estimated using regression coefficients that depend on land cover classification (MOD12Q1) and daily snow cover (MOD1L2). The algorithm also requires dynamic estimate of the atmospheric transmission which is derived directly from the MOD7 L2 product of vertical atmospheric temperature and vapor profiles and the MODIS cloud masking product. In addition to night and day LST estimates, the product also includes information on the emissivity, view-angle, cloud cover, time of acquisition, and quality control assessment for each grid cell. Under clear sky conditions the accuracy of the MODIS/LST product was reported to be within 1 C for a temperature range of -1 to 5 C. It was also reported to perform better in dessert regions (Wen and Zhang, 24). The relationship between LST and air surface temperature were studied by many both from theoretical and empirical perspectives. A few large scale global studies concluded that these relationships are dependent on local variables such as land use/cover, soil moisture, and regional microclimate conditions and local landscape features. In addition, the regional variation of the 9

10 snow cover contributes to the small-scale spatial variation in the air interface temperature field (e.g. Mildrexler, et al. 211; Jin and Dickinson, 21; Prihodku and Goward, 1997). The MODIS LST in snow and ice covered areas was reported to be accurate within +/-1 o C (Wan, et al. 22) for a temperature range of -15 o C (Hall, et al. 28). MODIS LST was also used to detect the thermal regime of permafrost. Several studies conducted in the Arctic found MODIS LST to correspond well with permafrost and the detection of thawing (e.g., Langera, et al. 213, for the Lena River Delta, Siberia, and Hachem, et al. 212, for Northern Quebec, Canada). Compared with shallow ground based measurements the LST was found to have better association with air surface temperature and reported to perform better during periods with snow cover (Hachem, et al. 212). Another study from the Arctic that compared among various LST products derived from polar orbiter satellites concluded that the MODIS LST product had the best association with air surface temperature (Urban, et al. 213). Tight association between monthly MODIS LST and Ta was also reported at the Lambert Glacier basin in East Antarctica (Wang, et al. 213). Hall, et al. (29) used the MODIS LST product to detect melting areas in Greenland s ice sheet. However, very few studies compare and evaluate the performance of MODIS LST in midlatitude snow covered areas. A field study from the Italian Alps found strong correlation between the skin temperatures of snow and mean daily air temperature (Colombi, et al. 27). This association was found promising to enable derivation of detailed maps for surface air temperature in snow dominated mountainous terrain. These maps can potentially improve the estimation of the spatial variability of melt. 2. DATA AND METHODS 2.1 STUDY REGION: The study was conducted in the Southeast region of Turkey (black thick outline in Figure 2). The study domain (5km x 33km) contains the headwaters of the Euphrates and Tigris Rivers and other east-flowing rivers draining into the Caspian Sea. The domain expands across two continental climate zones: southeastern Anatolia and Eastern Anatolia. In the western part of the 1

11 domain is the Anti-Taurus mountain range with average peak elevation at 3, meters; while the eastern part near the border with Armenia is the Armenian Highlands mountain range that peaks at Mount Ararat (5,137 meters). Turkey s largest lake, Lake Van, is situated in the mountains at an elevation of 1,546 meters. The Southern slopes of the Anti-Taurus Mountains are a region of rolling hills and a broad plateau surface that extends into Syria. The region experiences severe winters with frequent heavy snowfall events and warm summers. The dominant land covers are grassland with patches of closed and open shrubland and large extent of cultivated agricultural land. The selected case study region is included in the recently implemented operational BSMEFFG system, which covers 8 countries (Bulgaria Turkey, Azerbaijan, Armenia, Georgia, Syria, Iraq and Lebanon) and includes about 7, basins with an average basin size of 25 km 2. A detailed description of the system and the snow model can be found in Georgakakos, et al. (213) and Shamir, et al. (212 and 213). 11

12 Figure 2: A map of the study area 2.2 IN-SITU OBSERVATIONS Meteorological time series of observations for 1 October 22 3 September 21 were received from the Turkish State Meteorological Service (TSMS). For the study domain we identified 31 stations with hourly air surface temperature and daily snow survey information such as snow water equivalent and snow depth (white triangles in Figure 2). Eighteen of those locations also have daily precipitation records. The range of the station elevations is 37-23m with a mean average elevation of 13m. This dataset represents a fairly dense observation network with average distance between nearest neighbors equal to 37 km (range 11 to 8 km). Such network density is considered dense by operational network standards, especially for high mountains and rugged terrain regions, as in the study domain. 2.3 MODIS LST DATA FOR THE STUDY AREA The LST product from the Aqua and Terra MODIS sensors was retrieved from NASA s Earth Observing System Data and Information System for 1 October 22-3 September 21. We 12

13 used version 5 of the daily Aqua and Terra Land Surface Temperature/Emissivity L3 which is available globally at 1km in a sinusoidal projection (MOD11A1 and MYD11A1). The study area is covered by a single tile that includes 12 rows and columns (h21v5). Most overpasses for Terra [Aqua] occurred around 11: and 23: [13: and 1:] local time LST PRODUCT AVAILABILITY For the study region and the duration of analysis the LST dataset has 55 missing daily products (out of 6574), most having occurred prior to 25. The availability of valid LST reports for the study duration and for the grid-cells that match the 31 surface temperature gauges was summarized by month and is presented in Figure 3. It is seen that valid LST products are available less than 4% of the time during November-April. The availability of valid LST product rises to about 6-8% during June-September for Aqua and Terra, respectively. We note that only 2% and.5% of the missing LST product lasted longer than 15 and 26 consecutive days, respectively. Figure 4 shows the status of the MODIS snow cover product for the same period and domain available from the Aqua 5m daily snow cover product (MYD1A1) (Hall et al., 1995). As mentioned above the snow cover product is being used as input to the MODIS LST algorithm. In Figure 4 we consider the entire dataset as valid and invalid reports. Valid reports are considered as grid-cells that report snow cover, bare ground, or water; and invalid reports are grid-cells that considered as clouds masking and missing data that are flagged by the quality assessment index. In addition, as a point of reference, we present the percent of snow cover reports (out of the total) which is also part of the valid report category. 13

14 Valid LST Reports (Fraction) guages Oct 22-Sept 21 Terra Day Terra Night Aqua Day Aqua night Months Figure 3: Aqua and Terra monthly summary of the valid day and night LST reports (Oct 22-Sept 21) for grid-cells that are associated with the 31 gauges of surface air temperature. During November-April, the period that snow cover reports are shown (blue line), the cloud cover was reported in 5-7% of the time (red line). On the other hand, cloud cover decreased considerably during the summer months and valid daily reports of the product (green line) had become abundant. Notice however, that for this period (summer) the number of snow cover reports is marginal (blue line). The black line which ranges between 8-12% summarizes the periods in which the SCA product quality assessment index indicated a problem with the SCA product. The prevalence of cloud cover in the study region which obscures the MODIS SCA product during the snow accumulation season was previously documented by Tekeli, et al. (25 and 26). 14

15 The analyses presented in Figures 3 and 4 indicate that the LST product is highly infrequent for the winter when snow is likely to accumulate and become available with higher frequency during spring-summer which is the melting period. Figure 4: Classification of the Snow Cover Area (SCA) reports from Aqua to valid report (red) clouds (green) and no data (black). In addition, the valid reports that indicated snow cover are in blue LST CORRESPONDENCE WITH AIR SURFACE TEMPERATURE The correspondence between the LST and gauge surface temperature for the study region is shown in Figure 5. The black [blue] dots in this figure indicate cases in which the snow [no snow] was reported in the gauges. The red lines are the estimated linear regressions and the 5 and 95 percentiles confidence intervals calculated separately for gauges with and without snow. 15

16 Figure 5: Comparison of MODIS LST and 31 surface air temperature gauges. Black dots indicate gauges with snow measurement and blue dots are for gauges with measurement of no snow. Red lines are regression estimates and confidence bounds (5% and 95%) for the snow and no snow separately. Although there is an overall monotonic association between the gauges and the LST values, there is an apparent large scatter and, in some cases, lack of apparent correspondence between these two variables. It is also seen that the relationships during snow and no snow periods are different as seen by the slopes of the regressions. In general during periods of snow on the ground small changes in LST correspond with larger range of surface temperature values. During these periods the LST values are below or equal o C, while the surface temperature can possibly rise above freezing temperature. 16

17 The intra-annual correspondence between LST, gauge air surface temperature, and SWE is further demonstrated in Figure 6. During periods of no snow the LST day-night amplitude brackets the air surface temperature, with LST being warmer [colder] than surface air temperature during the day [night]. During periods that snow was measured at the gauges (black solid line) the day-night amplitudes of the LST and air surface temperature are comparable. The LST temperature remained consistently below zero while air surface temperature occasionally increased above zero. Although snow was measured at the gauge as late as mid-march, the LST increased above zero about a month earlier. This is probably due to patchiness of snow cover that is prevalent during spring times and classification of the LST product as snow free grid-cells while snow existed in high elevations. During spring it is expected that in-situ snow reports will often misrepresent the snow cover at their corresponding grid-cells LST Tera Day LST Tera Night Ta Day Ta Night SWE (cm) Temperature o C A S O N D J F M A M J J Days: 1 Aug July 26 Figure 6: An example of annual plot of day and night Terra LST (red and black circles, respectively), day and night gauge air surface temperature (red and black stars, respectively) and snow water equivalent (cm) (black line). Another depiction of the relationship between LST and surface temperature is provided in Figure 7. Two years of LST and Ta daily time series for, 6, 12, and 18 hour local time are plotted at this Figure from four gauges that were selected in order to represent different elevations. The interpolation of the MODIS LST from the observation times to these hours and for the periods for which LST valid reports are unavailable is described in Section

18 4 Local time 4 6 Local Time 4 Local time 4 6 Local Time Temperature ( o C) Elevation MODIS 6 LST Gauge Ta 12 Local Time Local Time Temperature ( o C) Elevation MODIS 6 LST Gauge Ta 12 Local Time Local Time October 22 -September 24 (Days) October 22 -September 24 (Days) 4 2 Local time Local Time 2 Local time Local Time Temperature ( o C) Elevation MODIS 6 LST Gauge Ta 12 Local Time Local Time Temperature ( o C) Elevation MODIS 4 LST 6 Gauge Ta 12 Local Time Local Time October 22 -September 24 (Days) October 22 -September 24 (Days) Figure 7: Two years (1 October 22-3 September 24) of LST and surface air temperature at, 6, 12, and, 18 hour local time for four selected stations that represent different altitudes. Legend elevation in meters. The general patterns among these selected gauges are similar except that in the higher elevations gauges (lower panels) during the winters there are more missing LST values and the reported LST values for these periods are based on climatological values for their estimates and thus appear as straight lines. The plot further demonstrates the diurnal differences during different periods of the year. During nighttime ( hour) the gauge surface air temperature is consistently higher than the LST. In and around daybreak hours (6 hour) the LST and Ta are matching fairly well except during winters in which the LST remains lower than freezing while air surface temperature occasionally rises above freezing. At noontime the LST is consistently hotter than 18

19 the air surface temperature with the exception of winters in which, again, the LST stays below freezing temperature. Last, in the early evening (18:) the same pattern as seen at noontime is exhibited with smaller differences between the LST and Ta. 2.4 SNOW-17 MODEL The Snow 17 model as described in Anderson (1973 and 26) simulates snow accumulation and ablation based on the solution of snow energy balance equations and using meteorological variables as indices for physical processes. A distributed implementation of the model in the Sierra Nevada is described in Shamir and Georgakakos (26 and 27). The model performed well in the inter-comparison study of eleven operational models conducted by the World Meteorological Organization (1986) and is routinely applied by the US National Weather Service River Forecast Centers (e.g. Shamir, et al. 26) and the North American Land Data Assimilation System (NLDAS) (Sheffield, et al. 23). It is also used in the BSMEFFG system and in two recent regional spring snow outlooks for southeast Europe and Middle East (Shamir, et al. 212 and 213). The model is appealing for operational use because it only requires the diurnal evolution of precipitation and surface air temperature (T a ) as forcing. Herein we describe the equations that use the surface air temperature variable. First, T a is used to distinguish between rainfall and snowfall using a preset temperature threshold. Second, the surface temperature is used as index for the snow cover energy exchange to determine the rate of melt (M) during dry weather. The rate of melt (mm 6-hr -1 ) is determined by: M = M f (T a - T base ) (1) where T base ( o C) is a temperature threshold that instigates melt and M f (mm o C 6-hr -1 ) is the melt factor which is a factor that relates surface air temperature to melting rate. The annual changes of the melt factor due to changes in incoming solar radiation and decrease of albedo of the older snow is estimated by the following sine function: sin (2) 19

20 where, MFMAX and MFMIN are assigned model parameters that represent the maximum and minimum melt factor which in mid-latitudes are assumed to occur on June 21 and December 21, respectively, and n represents the day count since March 21. The MFMAX and MFMIN are commonly estimated as functions of the land cover, slope and aspect. Third, surface air temperature is assigned as snow surface temperature during non-melt periods for the calculation of the snow pack energy balance. The surface temperature used for the calculation of antecedent temperature index of the snow profile (ATI) and is being updated using: ATI (t+1) = ATI t +TIPM (T a ATI t ) (3) where TIPM is the antecedent temperature index parameter (range.1-1). The gain or loss of heat during non-melt periods can now be expressed as: D = NM f (ATI t - T a ) (4) where D is the change in snow cover heat deficit (expressed in mme, which is the amount of heat required to melt or freeze 1 mm of ice or water at o C). NM f is a proportionality factor referred to as the negative melt factor (mme C -1 ). The process of heat transfer in a snow cover is primarily done by conduction. The thermal conductivity of snow is mainly a function of snow density and since snow density tends to increase as the snow season progresses, the rate of heat transfer should also increase. The representation of the seasonal variation of the NM f is estimated using: (5) where NMF is the maximum negative melt factor (mme C -1 6-hr -1 ). Next, the snow water equivalent (SWE) sensitivity to surface air temperature through each of the three processes that are described above is examined (Figure 8). For each case we ran the model 2

21 with three time series of surface air temperature: the observed time series and the observed biased with +/- 3 o C (green and red lines, respectively). Figure 8: Sensitivity of simulated SWE to biased air temperature (+/- 3 o C) as affects each of three processes: temperature threshold to distinguish rain from snow (left), snow melt (middle), and snowpack energy balance during non-melt periods (right). The black lines in the three panels represent a SWE simulation with the unbiased (observed) surface air temperature. In the left panel we used the three T a time series to distinct snowfall from rainfall and, as expected, the colder time series yielded considerable higher accumulation of snow because more of the precipitation events were assigned as snow. In the middle panel the three T a time series were only used for the calculation of the snow melt (Equation 2). The biased temperature produced different melt rates, resulting in different snow pack quantity and significantly impacting spring snow depletion. Last, in the right panel, we used the Ta time series as LST (Equations 3 and 4) for the calculation of ATI and NMF. It is seen that the SWE simulations are insensitive to LST changes. The sensitivity analysis presented in Figure 8 suggests that using the MODIS LST to force the LST of the snowpack in the model will yield only a slight gain since simulations of the seasonal SWE are relatively insensitive to changes of LST values. On the other hand, using the LST to infer surface air temperature will potentially improve the model simulations of snow pack properties. We note that the conclusions drawn from this sensitivity analysis are only valid for the SNOW-17 model and should carefully be tested for other snow models. 21

22 3. SURFACE TEMPERATURE INTERPOLATION 3.1 FILLING OF MISSING LST VALUES The filling of missing LST values and the interpolation of the LST values to, 6, 12, and 18 hour local time from the observation times, were done using harmonic function analysis (e.g. Wilks, 211). Harmonic functions were used to interpolate MODIS LST in several previous studies (e.g., Hachem, et al. 212; Lenski and Dayan, 211). Denote by y t the LST at hour t within a day then, (6) where A and are the daily amplitude and the daily average of the LST, respectively, is the phase angle or sometimes called phase shift. In order to use the harmonic function, the amplitude and average LST were updated daily whenever at least one day and night measurements of LST from either Aqua or Terra were available. In case of missing LST data these parameters were carried forward up to three days; beyond that the parameters were assigned their climatological values AIR SURFACE INTERPOLATION FROM MODIS LST The LST values which were interpolated to, 6, 12, and 18 hour local time were used to derive Ta. Each gauge location was associated with the 1 closest LST grid cells and for each time step the median of the valid reports was assigned as the representative LST value. For a given interpolation target location the median of the ratio between the gauge Ta and the corresponding LST values (median of 1 closest grid values) within a range of 1 km was identified and multiplied by the LST value at the target location to derive the Ta estimate. By limiting the procedure to cases for which the difference between LST and Ta did not exceed 15 o C, many cases with very weak associations were eliminated from the interpolation procedure. 3.3 AIR SURFACE INTERPOLATION FROM GAUGES The evaluation of the surface air temperature from the MODIS LST data was done using the 18 gauges that have datasets that include daily precipitation and provide complete forcing for the snow model. The derived surface air temperature was compared with interpolated surface air temperature from the gauge data. The derivation of the interpolated surface air temperature time 22

23 series resulted from two separate runs that represent a range of uncertainty attributed to gauge network density. First, the entire available gauge dataset was used to represent interpolation under optimal conditions. The interpolation was done by selecting all gauge data within a radius of 1 km and adjusting their temperature to the elevation at the interpolation location using a typical lapse rate (.5 o C/1 meter). The adjusted temperature values were then interpolated to the location of interest using inverse distance weighting. The second procedure was based on a single gauge that was randomly selected and was interpolated to the location of interest using the lapse rate. The latter case represents typical operational conditions of gauge density. In regional FFG systems and in data scarce regions, the forcing of the snow model often relies on climatological estimates of temperature and, therefore, the LST-derived Ta was also compared to climatological values. In order to assess the impact of using climatologies, two time series that are based on climatological values were added. The first one is based on climatological values of the on-site gauges and the second is from the Climate Research Unit (CRU), East Anglia (New, et al. 22). The CRU climate data (1 minute grid size) provides monthly temperature (mean and range) that are based on analyses of gauge records The CRU monthly climatological values were further interpolated to, 6, 12, and 18 hours using the harmonic-function formulation. A comparison between the Ta and the CRU dataset is shown in Figure 9 and demonstrates the wide range of uncertainty that is introduced by using climatological CRU values. It is seen that for a given CRU base value the observed Ta range can exceed 3 o C. We note that the CRU data set is being used in regional FFG systems for regions that do not have other sources of data. 23

24 Figure 9: Comparison of observed Ta at, 6, 12, and 18 hour local time with CRU monthly climatological values. Scatter plots between the observed surface air temperature and the surface air temperature estimates from the various interpolation schemes are presented in Figure 1 for the entire period. Analogous plots by month are provided in Appendix A. In general it can be seen that, as expected, the interpolated Ta that made use of the 31 gauges has the best match with the observed Ta. We also infer that the LST-derived Ta is superior to the gauge climatology and the 1-gauge interpolation, especially for the lower temperatures which are crucial for the snow model. Similar conclusions are drawn from the monthly comparisons available in Appendix A. Appendix B presents further evaluation of the association between Ta and LST. The monthly comparison results between the observed and the four interpolation procedures are further summarized through the evaluation of four performance indices in Figure 11: the Pearson correlation coefficient (upper left), the root mean square error (upper right), the bias (lower left), and the standard deviation ratio (lower right). 24

25 Figure 1: A comparison between observed surface air temperature and interpolated surface air temperature for October 22 September 21. The interpolation includes 31 gauges (upper left), 1- gauge (upper right), gauge climatology (lower left), and LST (lower right). The Pearson correlation coefficient was selected to assess the linear association between the series with higher positive correlation values indicating a better linear association. The root mean square error (RMSE) provides a measure of accuracy for the interpolation. The RMSE is calculated using: (7) 25

26 Where n is the valid number of pairs Ta o and Ta i are the observed and interpolated surface air temperature, respectively. The bias index which is a measure of systematic differences was calculated as follows: (8) Last the ratio between the standard deviations was used as the fourth index to indicate whether the interpolated series maintained similar spreading and variability as in the observed series. Thus, standard deviation ratios that are closer to one imply agreement between the variability of both time series: (9) The red line in Figure 11 represents the LST-derived Ta and in most cases it performs second only to the 31-gauge interpolation (atypically dense gauge network). The LST-derived Ta Correlation coefficients range between.7 and.8. The RMSE values during June September appear larger for LST than all other interpolation estimates but note that during winter-spring time the LST-derived Ta is comparable to the gauge interpolation procedures. Looking at the bias index, it seems that the LST-derived Ta is superior and does not have consistent and systematic biases as seen in the other interpolation procedures. Last the standard deviation ratio shows that for 9 months the LST-derived Ta yield larger variability than the observed Ta. For all other interpolated Ta time series the interpolation procedure resulted in reduced variance. Therefore, it can be argued that the LST-derived Ta maintains the temporal variability of the observed time series. 26

27 1 Correlation Coef Gauges 1 Gauge Gauge Climatol. LST Interp Months Root Mean Square Error ( o C) Months Bias ( o C) S.D. Ratio Months Months Figure 11: Monthly comparison between the observed and four interpolated surface air temperature time series with respect to the Pearson correlation coefficient, RMSE, bias and ratio of standard deviations. 27

28 4. SNOW PACK IMPACTS To assess the impact of the different interpolation procedures, in particular the LST-derived Ta, on the snow model simulations of various snow pack properties, snow simulations using forcing from the different interpolation procedures were compared to a simulation that was forced by the observed Ta. The comparison was done for the 18 selected gauges using four indices that represent key characteristics of the snow pack (a schematic of the indices is shown in Figure 12). The selected indices are: 1) maximum SWE; 2) the date of maximum SWE; 3) the duration of melt; and 4) total melt during the spring period. Figure 12: Schematic of four snow pack indices: maximum and time of the maximum SWE (upper panel), and identification of spring onset using cumulative departure from mean of snowmelt. These indices represent the snow pack mass quantity and spring melt characteristics such as the timing of spring onset and spring melt quantity and duration. The spring onset is identified using the cumulative-departure method which, identifies the day when the cumulative departure of snowmelt from that year s mean melt is most negative. This is equivalent to finding the day of the year when the magnitude of melt shifts from less than average to greater than average (Cayan et al. 21). The spring melt duration is estimated as the period between this spring onset and the time of complete depletion of the snowpack. 28

29 Figure 13 presents cumulative distributions for the four indices that summarize 9 years of model simulations at the 18 gauge locations. Hence the Figure summarizes calculation of 162 values for a given temperature forcing series per snow-index. All the indices were compared to the indices derived from the observed Ta forcing. Best performance is characterized by the smallest differences. For the maximum SWE and the total melt (upper left and right panels, respectively) we compared the absolute relative differences between the simulations of the observed and interpolated Ta. For the maximum SWE of the LST-derived Ta, about 8 percent of the cases had lower than 1% biases; This result is slightly inferior only to the simulation with the 31- gauges-derived Ta. The simulations of total spring melt from the LST-derived Ta are substantially better than all other simulations. The LST-derived Ta performance of timing indices (lower panels) is less impressive than the simulations of mass and magnitude indices. It is seen that in about 2% of the cases the maximum SWE events were deviating by more than 25 days and the simulation is fairly similar to simulation of interpolation from 1 gauge. Similarly the performance of the LST-derived Ta simulation with respect to duration of spring melt index is superior to the climatic based interpolation and comparable with the 1-gauge Ta interpolation. 29

30 1 Max. SWE 1 Total Melt Cumulative Distribution Cumulative Distribution Relative Difference (%) Relative Difference (%) 1 Timining of Max. SWE 1 Duration of Melt Cumulative Distribution Difference (Days) Cumulative Distribution guages 1-gauge.2 Gauge Clim. CRU Clim. MODIS LST Difference (Days) Figure 13: Cumulative distribution of the differences between snow indices derived from simulations forced by the observed Ta and 5 different interpolated Ta for 18 gauged sites. The indices are the relative difference of maximum annual SWE (upper left), relative difference of total spring melt (upper right), the difference (days) between the timing of maximum SWE (lower left) and the duration (days) of the spring melt season (lower right). 3

31 5. SUMMARY, CONCLUSIONS AND EXTENSIONS 5.1 SUMMARY Existing operational regional flash flood guidance systems support weather forecasters to issue flash flood warnings and to protect approximately a half-billion people worldwide. Additional systems to protect an additional 1.7 billion people are currently under development. HRC works directly with local, national, and international meteorological, hydrological, and disaster warning and management agencies during the implementation of the systems. The models, their input and output include customization tools built into the models to facilitate local knowledge to be incorporated into the systems. In addition, HRC maintain an on-going educational component that is geared for training forecasters and disaster management personnel to routinely utilize the system in order to screen for risks of flash flood and to produce prompt flash flood warnings and watches. The feasibility study that is outlined herein is an example of on-going research activities that seek to improve the components of the FFG systems with new technology and remotely-sensed data. The values of the FFG in areas that experience seasonal snowpack are highly dependent on the extent and depth of the snowpack and melt. In addition, FFG interpretation is also being impacted by the presence of snow because the nowcast precipitation should be separated to a portion that falls on bare ground and a portion that falls on the snow pack. In the existing regional operational systems the snowpack and melt are being estimated by the Snow-17 model; an energy and mass balance snow accumulation and ablation model. This model uses mean air surface temperature as an index to represent various atmospheric and snowpack energy fluxes. In an operational setup it is often a challenge to receive in-situ real time temperature information especially in remote areas which are often the areas that experience seasonal snowpack. Retrieving distributed surface temperature from satellite data is therefore an attractive proposition that should be further explored. In this study we evaluate the feasibility of using the land surface temperature (LST) product from MODIS to estimate surface air temperature. The data are from four daily overpasses and thus can potentially provide information on the local diurnal cycle. 31

32 The study was conducted in the rugged terrain of Southeast Turkey. Daily snow depth and SWE and daily rainfall datasets were received from the Turkish State Meteorological Services for October 22- September 21. A comparison between the air surface temperature and the corresponding LST grid-cells indicates strong relationships for periods with and without snow. During winters cloud cover reduces the availability of the LST product to about 6% of the time. The frequency of valid LST values improves during the melting season (spring and summer). 5.2 CONCLUSIONS We compared a methodology of LST-derived surface air temperature to temperature interpolation procedures that are based on in-situ surface air temperature measurements. The LST-derived Ta performs well and is highly associated with the observed record, especially for the colder temperatures. The consequence of the various derived temperature time series on the simulation of the snowpack was examined by comparing the snow simulations to the simulation forced by the observed surface air temperature time series at the site of interest. The performance of the snow simulation is assessed by four indices that describe various snowpack characteristics. The LST-derived Ta performed well as model forcing to simulate indices that represent snow mass and magnitude characteristics. It outperforms single gauge estimates necessary in situation of sparse networks and it is useful for operational use. The performance of LST-derived Ta was not as good in capturing timing of the occurrence of the annual peak of SWE and the duration of spring melt. Such measures are better represented event by a single gauge in the region, as typically done in existing operational systems. Our assessment of the MODIS LST product in the Southeast Turkey points to its potential for operational use within flash flood guidance systems to better estimate the diurnal and spatial variability of surface air temperature. This information when used to provide forcing for the snow model component is expected to improve model output in terms of snow pack and snow melt volumes in regions with scarce in-situ, real-time temperature reports. 32

33 5.3 EXTENSIONS The study presented herein assessed the potential use of the MODIS LST as a surrogate for surface air temperature to constitute the forcing to the snow model in data scarce regions. The procedure and algorithm that processes these datasets should be further refined to consider various terrain features and land cover/use. It could also be evaluated in other geographic regions. In addition, there is a need to evaluate various interpolation procedures and devise better automatic quality control measures that will eliminate cases with noisy LST values. The use of other satellite products that can assist in snow modeling should also be examined. For example, satellite products should be examined that can distinguish between rainfall and snowfall, in order to substitute the parameterized temperature threshold that is currently being used. Another possibility is to identify wet and dry portions of the snowpack that can influence melt processes in the snow model. As a future research effort we suggest examining the improvements, over the present methodology, that the LST and other satellite derived meteorological variables offer when used as input to land surface models that couple moisture and energy and provide estimates of surface air temperature as their output. Thus, these models would substitute the statistical relationships used in this work to derive surface air temperature from LST The effect of the use of LST on the modeling of soil moisture and the flash flood guidance indices (in addition to the effects on snow melt examined herein) should be evaluated and the geographic constraints for this approach should also be identified and tested for different regions. Although additional work with NASA collaboration may improve the reliability, accuracy, and precision of the LST of snowpack in mountainous terrain, already it was shown in this work that the current LST product can be useful for remote areas with scarce information. We believe that an immediate next step is to develop appropriate implementations of the LST-derived surface air temperature algorithm for several of the regional FFG systems that have seasonal snow pack and flash flood risk that is sensitive to snow pack conditions (e.g. South Asia, Central Asia, Southeast Europe and Black Sea Middle East). 33

34 6. REFERENCES Anderson, E. A., 26: Snow accumulation and ablation model SNOW-17. US National Weather Service River Forecast System Documentation, NOAA, NWS-Office of Hydrologic Development, Silver Spring, MD, 44 pp. + 2 Appendices. Anderson, E. A., 1973: National Weather Service River Forecast System Snow Accumulation and Ablation Model. NOAA Technical Memorandum NWS HYDRO-17, NOAA, NWS- Office of Hydrologic Development, Silver Spring, MD, 217 pp. Bales, R.C., N.P. Molotch, T.H. Painter, M.D. Dettinger, R. Rice, R. J. Dozier. 26. Mountain hydrology of the western United States. Water Resources Research. 42, W8432. Cayan, D.R., S. Kammerdiener, M.D. Dettinger, J.M. Caprio, and D.H. Peterson. 21. Changes in the onset of spring in the western United States. Bulletin of American Meteorological Society, 82(3), Colombi A, C. De Michele, M. Pep, and A Rampini. 27. Estimation of daily mean air temperature from MODIS LST in Alpine areas. New Developments and Challenges in Remote Sensing, Z. Bochenek (ed.) 27 Millpress, Rotterdam, ISBN p Georgakakos K.P., R Graham, R. Jubach, T.M. Modrick, E. Shamir, C. Spencer and J. Sperfslage, 213. Global flash flood guidance system Phase I. HRC Technical Report No. 9. Hydrologic Research Center, San Diego, CA February 213. Hachem S, C.R. Duguay, and M Allard Comparison of MODIS-derived land surface temperature with ground surface and air temperature measurements in continuous permafrost terrain. The Cryosphere, 6:51-69 Hall, D.K., S.V. Nghiem, C.B. Schaaf, N.E. DiGirolamo and G. Neumann. 29. Evaluation of surface and near surface melt characteristics on the Greenland ice sheet using MODIS and QuickSCAT data. Journal of Geophysical Research, 114,F46, doi:1.129/29jf1287,29. Hall, D.K., J.E. Box, K.A. Casey, S.J. Hook, C.A. Schuman, and K. Steffen. 28. Comparisson of satellite derived and in-situ observations of ice and snow surface temperatures over Greenland, Remote Sensing of Environment, 112 :

35 Hall, D. K., G. A. Riggs, V. V. Salomonson Development of methods for mapping global snow cover using moderate resolution imaging spectradiometer data. Remote Sensing of Environment, 54: Jin, M., and R. E. Dickinson. 21. Land surface skin temperature climatology: Benefitting from the strengths of satellite observations. Environmental Research Letters, 5, 444, doi:1.188/ /5/4/444. Kuhn, M Micrometeorological conditions for snow melt. Journal of. Glaciology. 33, Langera, M., S. Westermannb, M. Heikenfelda, W. Dorna, J. Boikea Satellite-based modeling of permafrost temperatures in a tundra lowland landscape. Remote Sensing of Environment, 135:12-34 Lenski I.M., and U Dayan Detection of finescale climatic features from satellites and implications for agricultural planning. Bulletin of American Meteorological Society (BAMS), September 211: Lundquist, J.D., D.R. Cayan, M.D. Dettinger, 24. Spring onset in the Sierra Nevada: when is snowmelt independent of elevation? Journal of Hydrometeorology 5 (2): Lundquist, J. D., and D. R. Cayan. 27. Surface temperature patterns in complex terrain: Daily variations and long-term change in the central Sierra Nevada, California, Journal of Geophysical Research,112, D11124, doi:1.129/26jd7561. Mildrexler, D. J., M. Zhao, and S. W. Running A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests. Journal of Geophysical Research, 116: G325, doi:1.129/21jg1486. New M., D Lister, D., Hulme, M., and I. Makin, 22: A high-resolution data set of surface climate over global land areas. Climate Research, 21:1-25. Pinheiro A.C.T., J. Descoitres, J.L. Privette, J. Sussking, L. Iredell, and J. Schmaltz. 27. Nearreal time retrievals of land surface temperature within the MODIS Rapid Response System. Remote Sensing of Environment 16: Prihodko, L. and S. N. Goward Estimation of air temperature from remotely sensed observations. Remote Sensing of Environment, 6(3): Shamir E., T.M. Carpenter, C. Spencer, and K.P Georgakakos., 213 Snow assessments for Southern Europe and the Black Sea Middle East regions for the spring of 213. HRC 35

36 Limited Distribution Report No. 41 Hydrologic Research Center, San Diego, CA 28 March 213 (S13-187). Shamir E., T.M. Carpenter C. Spencer, and K.P. Georgakakos Snow assessments for the Southern Europe and the Black Sea Middle East region for the spring of 212. HRC Limited Distribution Report No. 39, Hydrologic Research Center, San Diego, CA 3 April 212. Shamir, E., K.P. Georgakakos. 27. Estimating snow depletion curves for American River basins using distributed snow modeling. Journal of Hydrology, 334 (1-2): Shamir, E., T.M. Carpenter, P. Fickenscher, and K.P. Georgakakos. 26. Evaluation of the NWS operational hydrologic model for the American River Basin. Journal of Hydrologic Engineering ASCE, 11(5): Shamir, E., and K.P. Georgakakos. 26. Distributed snow accumulation and ablation modeling in the American River Basin. Advances in Water Resources, 29(4): Sheffield, J., and many colleagues. (23), Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent, Journal of Geophysiscal Research, 18: 8849, doi:1.129/22jd3274, D22. Tekeli, A. E., A. Şensoy, A. Şorman, Z. Akyürek, and Ü. Şorman. 26. Accuracy assessment of MODIS daily snow albedo retrievals with in situ measurements in Karasu basin, Turkey. Hydrological Processes, 2: doi: 1.12/hyp.6114 Tekeli, A. E., Z. Akyürek, A. Şorman, A Şensoy, and Ü. Şorman. 25. Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey. Remote Sensing of Environment, 97 (2): Urban, M., J. Eberle, C. Hüttich, C. Schmullius, and M. Herold Comparison of Satellite- Derived Land Surface Temperature and Air Temperature from Meteorological Stations on the Pan-Arctic Scale. Remote Sensing. 5, ; doi:1.339/rs Wan Z., Y. Zhang, Q Zhang, and Z-L Li. 22. Validation of the land-surface temperatures products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data, Remote Sensing and Environment, 299(5613) : Wan, Z., and J. Dozier A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34:

37 Wan Z. 28. New refinements and validation of the MODIS land surface temperature/emmissivity products. Remote Sensing of Environment,112 : Wan, Z. 23, Land surface temperature measurements from EOS MODIS Data Semi-Annual Report July-December, 23. Wan, Z., Y. Zhang, Q. Zhang, and Z.-L. Li. 24. Quality assessment and validation of the MODIS global land surface temperature. International Journal of Remote Sensing, 25: Wang. Y., M. Wang, and J. Zhao, 213 MA Comparison of MODIS LST Retrievals with in Situ Observations from AWS over the Lambert Glacier Basin, East Antarctica. International Journal of Geosciences 4, Wan, Z., and Z. LI A physics-based algorithm for retrieving land-surface emissivities and temperature from EOS/MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 35: Wilks, D. 26. Statistical methods in the Atmospheric Science 3 rd edition, Willey 672p. 37

38 APPENDIX A: MONTHLY INTERCOMPARISON OF ESTIMATED SURFACE AIR TEMPERATURE Monthly plots that compare between the observed Ta and four different estimates of Ta that were derived using various interpolation procedures: 31 gauges, 1 gauge, climatological values at the gauge, and MODIS LST data. The interpolation procedures are described in Section 3. 38

39 39

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45 APPENDIX B: ASSOCIATION BETWEEN TA AND LST FOR GAUGED SITES IN TURKEY In this Appendix we present additional results pertaining to the association of the MODIS-based LST and the surface air temperature Ta at 18 gauge sites in Turkey. The results support the methodology presented in Sections 3.1 and 3.2 for using the LST to estimate surface air temperature over the region of interest. In order to estimate the ratio Ta/LST used for the estimation of the surface air temperature from several observation sites within a certain range of the point of interest, we need to identify the distribution of the ratio. If the distribution may be considered approximately Gaussian, then the ratio can be used to develop stable statistics. If, however, the ratio is not normally distributed even approximately, then the ratio should be transformed (e.g., through a logarithmic transformation) to a normal distribution and the analysis be done in the transformed space. In order to compute the ratio the denominator should be different from zero. Also, cases with very large or very small ratios should be eliminated to get stable estimates. Applying the constraints, LST >.1 o C and Ta/LST < 3, and using the median of the 1 closest grid-point LST to get an LST estimate for a gauge site we developed time series of corresponding Ta and LST for each of 18 gauge sites in the region of Turkey depicted in Figure 2. Figure B.1 presents the scatterplots between the derived time series of LST and Ta, and, for each gauge site, it also shows the determination coefficient R 2 of the regression of Ta on LST. The R 2 coefficients range from.67 to.92 with only 3 out of 18 gauges having values less than.85. This suggests that a regression model at a site is likely to explain about 7% of the overall surface air temperature variance. Plots of the ratio Ta/LST for each gauge site are presented in Figure B.2 together with the means and standard deviations. Inspection of the plots shows that the ratios are approximately normally distributed with ratio values ranging from.76 to.94 and with only two gauges having mean less than.8. The coefficient of variation of the ratio variability at a site is between.7 and.9. 45

46 Figure B.1: Scatter plots of Ta versus LST for each gauge site in Turkey. R 2 represents the coefficient of determination of the linear regression of Ta on LST. 46

47 Figure B.1 (cont d). Figure B.2: Histogram of the ratio Ta/LST for each gauge site in Turkey. The mean and standard deviation of the ratio are shown in each case. 47