A study of snowfall from coincident surface and space measurements

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1 A study of snowfall from coincident surface and space measurements Martin Yapur Abstract Over the period of January 10 th to the 18 th 2003, a student research team obtained snowfall measurements and weather data at Storm Peak Laboratory (SPL) in the vicinity of Steamboat Springs Colorado. These measurements were obtained in sequential collections of every three hours around the clock. The team registered four snowfall events with different water-equivalent precipitation amounts. Three-hour composite images from the Tropical Rainfall Measuring Mission (TRMM) home-page were downloaded and precipitation amounts were estimated to compare with the precipitation amounts determined from surface measurements. Out of 12 snowfall measurements at SPL with coincident data from TRMM, 7 measurements by TRMM reported snowfall (58% detection). The analyses of these measurements for the "SPL pixel" showed a poor correlation (R = 0.58) for the surface snowfall measurements and the precipitation amounts estimated from the satellite were significantly larger than the surface measurements. The discrepancies between the space-borne and surface measurements are expected to be improved using measurements from space-borne microwave imagers. Introduction The Storm Peak Laboratory located atop of Mount Werner at 10,520 ft MSL near Steamboat Springs in Colorado, is a unique facility that brings the opportunity to develop a variety of research projects in atmospheric sciences. Its strategic location and state of the art technology provides conditions of study hard to find anywhere else. For the last twenty years, Professor Ward Hindman from the Earth and Atmospheric Sciences Department of the City College of New York in cooperation with the Desert Research Institute (DRI) of the University of Nevada and several generations of students, have recorded snowfall and weather measurements at SPL. The outcome throughout these extensive periods is the production of many projects with the intention of better understanding the complex dynamic of clouds and the chemical and physical phenomena associated with environmental issues. Estimating precipitation is not an easy task. It requires the use of sophisticated techniques capable to distinguish between precipitating and non-precipitating clouds. However, the use of microwave radiation techniques enables clouds to be penetrated and precipitation to be detected (Kidder and Von der Haar, 1995). The Tropical Rainfall Measuring Mission (TRMM) home-page (URL: trmm.gsfc.nasa.gov/) provides an estimate of precipitation from merged Precipitation Radar (PR), TRMM Microwave Imager (TMI), Special Sensor Microwave Imager (SSM/I) and Geostationary Orbiting Environmental Satellite (GOES) infrared (IR) measurements. The purpose of this project is to compare coincident space-borne and surface snowfall measurements in an attempt to calibrate the space-borne measurements for snowfall. Yapur paper page 1

2 Motivation The continuous innovation of scientific instruments for research purposes is giving scientists the opportunity to assess environmental situations in new ways. More area observed means more data collected and accuracy in collection procedures means more consistent results. The development of space-borne instruments is providing new opportunities to obtain the necessary data and information to measure complex interactions that occur in the atmosphere. Continuous observations and keeping track of these interactions in the atmosphere requires the manipulation of instruments of high resolution from space. A new generation of polar orbiting satellites is capable of estimating the amounts of precipitation in clouds over specific locations (Kidder and Vonder Haar, 1995). I will attempt to analyze the efficacy of remotely sensed precipitation data for the measurement of snowfall by comparing these data with the surface measurements of snowfall during our study period. Principles of IR and passive microwave precipitation detection (Stephens, 1995) To estimate precipitation from satellite measurements is a complex process primarily because the techniques for these types of studies are not universally applicable. Consequently, techniques must be developed to characterize a region of precipitation and to estimate a specific type of precipitation. Infrared (IR) precipitation detection facts from Kidder and Vonder Haar (1995): The radiation does not penetrate through clouds Estimations are based on precipitation falling from the bottom of the cloud All precipitation estimation is necessarily indirect The precipitating particles (eg. raindrops and snow crystals) are not directly sensed Microwave precipitation detection facts from Kidder and Vonder Haar (1995): Microwave radiation penetrates clouds Estimations are made from radiation upwelling through the clouds Raindrops and snow crystals interact with microwave radiation which allows their detection Poor spatial and temporal resolution in comparison to IR detection methods Objective The purpose of this study is to compare water-equivalent precipitation amounts obtained from the images of precipitating regions on the TRMM home-page (URL: and surface snowfall measurements from SPL (40.48N, W). Yapur paper page 2

3 Instruments Satellite Special Sensor Microwave Imager (SSM/I) Only the SSM/I is discussed here because the TMI does not overfly SPL. The SSM/I measures in the microwave portion of the electromagnetic spectrum from 19 to 85 GHz. The 19, 37 and 85 GHz channels have a horizontal and vertical polarization (horizontal meaning radiation parallel to the ocean's surface). The imager receives naturally occurring microwave radiation from the Earth's surface and atmosphere. There are three primary mechanisms in which the atmosphere and surface interact with microwave radiation: emission/absorption, reflection (or scattering), and transmission. How a particular particle or molecule will interact with the incoming radiation depends on the wavelength of the radiation and the nature of the particle, eg. is it an absorber and/or scatterer. Clouds can either absorb the radiation (as is the case with low level clouds) or scatter it depending on what water phase (liquid or ice) makes up a cloud. The SSM/I responds to the amount of liquid water within the cloud, not to its thickness. In the majority of cases the presence of clouds and precipitation will cause each frequency to behave differently. Infrared (IR) Much of the 3-hour precipitation amounts displayed in the TRMM home-page images in the region of SPL no doubt come from the GOES IR technique of estimating precipitation. This is because of the infrequent overflights of the SSM/I. Higher cloud tops are correlated with heavier precipitation for convective clouds. One exception to this rule occurs when these clouds flow out of thunderstorms. These cirrus clouds are high and therefore "cold" in the infrared observations but they do not precipitatie. To differentiate these cirrus clouds from water clouds, a technique, which involves comparing the two infrared channels at 10.8 and 12.0 micrometers is employed. IR techniques usually have significant errors for instantaneous rainfall estimates. The strength of the IR observations lies in the ability to monitor the clouds continuously from geostationary orbit. Surface A method of snowfall collection in the windy SPL-environment developed by Borys, et al. (1988) was used to obtain snow samples every three-hours during a snow event. This method consist in deploying snow-sample bags into the snow collector (two tubes aligned into the flow of the air) to catch the snow crystals blown by the wind. In addition to the SPL method, we obtained snowfall measurements from an automated system of snow collection used at the nearby Patrol Headquarters (PHQ). The Yapur paper page 3

4 PHQ instrument operates as follows: snow falls into a liquid mixture of ethylene glycol and water (antifreeze) and melts. When a pre-determined amount is collected, a trigger is activated and a water-equivalent depth of snow is recorded. Measurements Storm Peak Laboratory During the period of our study, following the SPL standard observation sequence, we recorded 4 separate snow events as listed in Table 1. Each event has been identified in the table by a color code. Table 1 Record of snow events between 10 and 18 January 2003 Date 2003 Sample # Time out (MST) Time in (MST) Net period (h) Mass of snow (g) Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Space-based The measurements of precipitation were estimated from images obtained from the TRMM home-page. The details on how the images were obtained are given in the Appendix. All the available images that coincide with the SPL snowfall events listed in Table 1 are illustrated in Figures 1 through 12. The "SPL pixel" will be used as a reference point to determine the presence of precipitation detected by the satellite-borne sensors: "SPL Pixel " 40.48N, W Yapur paper page 4

5 First snow event Date Sample Time out Time in Time out 2003 # (MST) (MST) UTC Jan Jan Jan Jan Jan Jan Snowfall activity in the SPL pixel Figure 1. TRMM Image, 10 JAN 2003, 1800 UTC. Note the scale is valid for all subsequent TRMM images and that the "inches" should be "inches/hour". Snowfall activity in the SPL pixel Figure 2. TRMM Image, 10 JAN 2003, 2100 UTC. Yapur paper page 5

6 No snowfall over SPL Figure 3. TRMM Image, 11 JAN 2003, 0000 UTC No snowfall over SPL Figure 4. TRMM Image, 11 JAN 2003, 0300 UTC Barely perceptible coloration over SPL Figure 5. TRMM Image 11 JAN UTC Yapur paper page 6

7 Second snow event Date Sample Time out Time in Time 2003 # (MST) (MST) out UTC Jan Jan Jan Jan 0635 Jan Jan Jan Note, there were no 11 Jan 2003, 21UTC and 12 Jan, 00UTC images in the TRMM home-page archive for the second snow event. The satellite doesn t record snowfall activity Figure 6. TRMM Image, 12 JAN 2003, 0600 UTC The SPL pixel appears snow free Figure 7. TRMM Image, 12 JAN 2003, 0900 UTC Yapur paper page 7

8 No snowfall activity registered over SPL Figure 8. TRMM Image 12 JAN UTC The SPL pixel is clear, no snowfall recorded Figure 9. TRMM Image 12 JAN UTC Yapur paper page 8

9 Third snow event Date Sample Time out Time in Time out 2003 # (MST) (MST) UTC Jan Jan Major activity registered in the SPL pixel Figure 10. TRMM Image, 15 JAN 2003, 1500 UTC Intense snow activity is recorded in the SPL pixel Figure 11. TRMM Image, 15 JAN 2003, 1800 UTC Yapur paper page 9

10 Fourth snow event Date Sample Time out Time in Time 2003 # (MST) (MST) UTC Jan The SPL pixel shows some snow activity Figure 12. TRMM Image, 15 JAN 2003, 1200 UTC Results Water-equivalent snowfall rates Storm Peak Laboratory Water-equivalent snowfall rates (r, mm/h) were determined from the SPL snowfall measurements in Table 1 using the following relationship from Borys, et al. (1988): r = Mass of snow(g)/(density of water (g/cm 3 ) * Area of collection (cm 2 ) * Net period (h) * (cm/10 mm) The resulting precipitation rates are listed in Table 2. Patrol Headquarters Water-equivalent snowfall rate estimates for PHQ were determined as follows. The set of data had recordings of snowfall water-equivalent depths every five minutes. We obtained the total water-equivalent snowfall depth in inches recorded at PHQ during the same period (T) that snow was being collected at SPL. Using the following relationship: h = (W-E PHQ (in) * 25.4 mm/in)/t (h) we developed the values of waterequivalent snowfall rate in mm/h for PHQ. The resulting values are listed in Table 3. Yapur paper page 10

11 Date 2003 Sample # Time out (MST) Table 2 SPL Water-equivalent Snowfall Rates Time in (MST) Time out UTC Net period (h) Mass of snow (g) ρ water (g/10mm *cm^2) A of Collection (cm^2) W-E Snowfall Rate (mm/h) Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Tropical Rainfall Measuring Mission TRMM Water-equivalent snowfall rates estimates were obtained from the three-hour composite infrared images (Figs. 1 to 12) by comparing the color value-scale in mm/h determined by the mission to the color observed in the "SPL pixel". For example, as seen in Figure 1, the color in the SPL pixel is between green and blue which corresponds roughly to a precipitation rate of around 2 mm/h. This value was interpreted to mean the precipitation at any time during the three-hour interval was 2 mm/h (this assumption will have to be checked by understanding how the TRMM images were constructed). The procedure for estimating the snowfall rates was repeated for each image and the results are tabulated in Table 3. Yapur paper page 11

12 Table 3 Water-equivalent Snowfall Rates from PHQ Measurements and TRMM Images W-E TRMM (mm/h) W-E PHQ (in) W-E PHQ (mm/h) Image missing Image missing Comparison of SPL, PHQ and TRMM coincident measurements Figure 13 shows the recording of snowfall by the three different data sources of this study. The values plotted in the graph have been previously transformed into waterequivalent snowfall rates (see Tables 2 and 3) with the intention of having the precipitation rates (mm/h) for each time of measurement. Water equivalent (mm/h) mm/h Jan. 10 Jan. 10 Jan. 10 Jan. 10 Jan. 10 Jan. 11 Jan. 11 Jan. 11 Jan. 12 Jan. 12 Jan. 12 Jan. 15 Jan. 15 Jan Date W-E SPL (mm/h) W-E TRMM (mm/h) W-E PHQ (mm/h) Figure 13. Coincident measurements of water-equivalent snow fall rates for SPL, TRMM and PHQ Yapur paper page 12

13 Table 4 shows a comparative chart of the recordings of snow activity by the three different data sources. Table 4 Recording of Snowfall activity by the different sources Date 2003 TRMM SPL PHQ Jan. 10 Yes Yes Yes Jan. 10 Yes Yes Yes Jan. 10 Yes Yes Yes Jan. 10 No Yes Yes Jan. 10 Yes Yes Yes Jan. 11 Image missing Yes Yes Jan. 11 Image missing Yes Yes Jan. 11 No Yes No Jan. 12 No Yes Yes Jan. 12 No Yes Yes Jan. 12 No Yes Yes Jan. 15 Yes Yes No Jan. 15 Yes Yes Yes Jan. 17 Yes Yes Yes Discussion The amounts of water-equivalent precipitation in mm/h obtained from the TRMM home page images are estimates based in the color scale provided by the mission. Thus, the numeric values obtained in our results are not the product of an algorithm we developed, limiting the scope of our study. As we can see in Figures 1 to 5, the first snow event recorded at SPL does correlate with the images from the satellite where we can observe clearly coloration in the "SPL pixel" in four of the five measurements (Table 4). The second event, characterized by a reduced snowfall rate at SPL, shows the limitations of the satellite to detect snowfall activity, particularly over snow-covered terrain. Figures 6 to 9 did not show any coloration in four of four measurements (Table 4). The third snow event, with the highest snowfall rate from the images provided by the satellite, Figures 10 and 11 show an intense activity over the "SPL pixel". However, our snowfall measurements from SPL and PHQ did not detect any snowfall coincident with the highest space-based measurement (Fig. 10). The forth snow event presents similar characteristics as the first event, Figure 12 shows a moderate coloration compatible with our recordings of snowfall rate and water equivalent at SPL. So, as summarized in Table 4, out of 12 snowfall measurements at SPL with coincident data from PHQ and TRMM, 8 measurements at PHQ reported snowfall (83% detection) and 7 measurements by TRMM reported snowfall (58% detection). The precipitation rates determined from the SPL, PHQ and TRMM were analyzed for mean, standard error and correlation coefficient values as shown in Table 5. The SPL and PHQ means and standard error values were quite close as would be expected Yapur paper page 13

14 Table 5 Analysis of SPL, PHQ and TRMM water-equivalent precipitation rate measurements (mm/h) Sample SPL PHQ TRMM image missing image missing Average StdError R from measurements made a short distance apart. However, the correlation coefficient between the two sets of data shows a poor correlation and, hence, provides little confidence with the PHQ measurements. Surprisingly, the TRMM data correlated better with the SPL data than did the PHQ data! The TRMM average water-equivalent snowfall measurements and standard-error values are greater than those for SPL and PHQ. The standard-error values are greater due to the large number of non-detection of snowfall by TRMM. The space-borne measurements correlate better with the SPL measurements than the surface measurements obtained with the automated system of PHQ. The results obtained in our study are an indicator of the deficiencies of satellite observations over mountainous regions and surfaces cover with snow. It is recognized in the microwave remote sensing community (Nesbitt, 2003, personal communication) that the IR method of detecting snowfall misses snowfall from shallow orographic clouds of the type that produce much of the snowfall at SPL. The algorithm developed for the TRMM home-page images was not able to isolate the scatter in our surface measurements, partially due to the crudeness of estimating precipitation rates using a "color wedge" and the inability of the algorithm to detect snowfall from shallow orographic clouds. Therefore, at the moment, the efficacy of satellite observations are not 100% effective for detecting snowfall at SPL. Conclusions According to the results of our study, we obtained water equivalent snowfall rates from the snowfall measurements recorded at the Storm Peak Laboratory and with an Yapur paper page 14

15 automated system of snowfall collection operated at the nearby Patrol Headquarters of the Steamboat Ski and Resort Corp. Coincident images from the Tropical Rainfall Measurement Mission of the times of snow activity at the SPL pixel were obtained and estimations of water-equivalent snowfall rates were made from these images. Out of 12 snowfall measurements at SPL with coincident data from PHQ and TRMM, 8 measurements at PHQ reported snowfall (83% detection) and 7 measurements by TRMM reported snowfall (58% detection). The SPL and PHQ average waterequivalent snowfall measurements and standard error values were quite close as would be expected from measurements made a short distance apart. However, the correlation coefficient between the two sets of data shows a poor correlation and, hence, provides little confidence with the PHQ measurements. Surprisingly, the TRMM waterequivalent precipitation measurements correlated better with the SPL data than did the PHQ data! The TRMM average water-equivalent precipitation measurements and standard error values are greater than those for SPL and PHQ. References Kidder, S. and T. Vonder Harr, 1995: Satellite Meteorology, Academic Press, pp Borys, R. D., E. E. Hindman and P. J. DeMott, 1998: The chemical fractionation of atmospheric aerosols as a result of snow crystal formation and growth. J. Atmos. Chem., 7, Stephens, G., 1995: Remote sensing of the lower atmosphere. An introduction, Oxford University Press, pp Recommendations 1. An inclusion of cloud top temperatures over the SPL pixel during the times of data collection would help determine the limits of the IR technique in detectng snowfall from shallow orographic clouds. 2. Calibrate the PHQ snowfall measurements using a careful cross-comparison with the SPL measurements. 3. Learn how to extract SSM/I water-equivalent snowfall data from polar orbiting satellites for the "SPL pixel". The data need to be extracted at the time of coincident SPL snowfall measurements to calibrate the space-borne measurements. The data also need to be extracted during cloud-free conditions to determine the background noise. Yapur paper page 15

16 Acknowledgements My special thanks to my graduate advisor Professor Ward Hindman for his dedication, interest and endless patience throughout the course. To Professor Randy Borys for his inspirational talks at SPL. To Bruce Roemich for making me in three days a "blue diamond" skier. To Cathi Widemer and the extraordinary Steamboat Springs Ski and Resort Corporation. To the NOAA EPP/MSI, ORISE and AMP-CCNY programs for their financial support. To my fellow teammates for their friendship, and last but not least to my beautiful wife Kelli and our amazing daughter Julianna for their endless love and support. Appendix: Procedures for obtaining satellite images All the images were obtained from the archives of three-hour composite images of the TRMM homepage (URL: As a courtesy of the webmaster Mr. Harold Pierce, a URL site was constructed with the requested images for the specific times for the purpose of this study. The TRMM reconstruction was facilitated by Dr. Robert Adler, NASA-GSFC. Yapur paper page 16