ROLE OF CLOUD RADIATION INTERACTION IN THE DIURNAL VARIATION OF PRECIPITATION

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1 ROLE OF CLOUD RADIATION INTERACTION IN THE DIURNAL VARIATION OF PRECIPITATION SETHU RAMAN AND ADRIENNE WOOTTEN DEPARTMENT OF MARINE, EARTH AND ATMOSPHERIC SCIENCES NORTH CAROLINA STATE UNIVERSITY, RALEIGH, NC Introduction Forecasting convection in the summer is a challenge for meteorologists due to the meso scale and local scale effects. These effects are enhanced and many times are caused by horizontal gradients in surface heat fluxes caused by the presence of different soils with different heat capacities or by land use. Parameterizations in most of the weather forecast models experience problems in accounting for variations of such heat flux gradients that cause low level convergence and convection initiation. Another problem the models face is the convection caused by cloud radiation interaction. This process is dominant during nights. In the Sandhills region of North Carolina, a strong gradient in soil type is present with soil changing from clay in the Sandhills to sand in the coastal regions. In this paper, a case study is presented to illustrate the diurnal variation of convection and precipitation and the processes involved. We then discuss the diurnal variation of convection over the Sandhills region using observations for six years. Cloud radiation interaction processes that may be responsible for the diurnal variation are discussed. Performance of a weather forecasting model in predicting diurnal variation of convection is presented. 2. A Case Study of Nocturnal Convection This section describes briefly the diurnal convection pattern over the Sandhils region during a typical summer day, 10 August Location of the stations used in this analysis are shown

2 Figure 1. Regional map showing Sandhills and coastal surface weather stations

3 in Figure 1. Radar reflectivity from the Doppler radar located at were significant (1.35 in or 34 mm) at the Colombia automated weather station Columbia, SC radar shows apparent (KCUB). This convection was highly lack of convection during day time with the exception of those storms along the sea breeze front close to the coast in localized over the Sandhills as can be seen from the regional distribution of the nocturnal precipitation shown in Figure 4 the afternoon (Fig. 2 a-c). This a-d. The precipitation distribution is convection is also visible through the GOES-8 IR imagery at 1900 GMT 1400 LT as shown in Figure 2. However, just before the sunset in the Carolinas, a line of storms begins to form along the southern edge of the Sandhills (Fig 3 a- mapped from 1700 LT on August 10 to 0200 on August 11, 2001 in Figures 4 a- d to investigate how local the convection was. Precipitation was not observed at other weather stations through the night, and Columbia (KCUB) recorded the d). As the night progresses strong maximum precipitation as the storms convection is present along the Sandhills region while convection reached maturity. 3. Clmatological Analysis of Diurnal elsewhere has disappeared. Around Precipitation 2300 LT (Fig 3c), a group of storms advances northward along the Sandhills, becoming more intense along In this study, observations for the summer, May through September, from 2001 to 2006 was used for the stations the way. This group of storms shown in Figure 1. The stations used proceeded further up the Sandhills into North Carolina by 0200 on 11 August were NC ECONet (North Carolina Environmental and Climate observing (Fig 3d), the next day. Precipitation Network) and the National Weather amounts by this night time convection Service ASOS (Automated Surface

4 Figure 2a. Radar image from Columbia, SC 1300 GMT (8EST) 8/10/2001 Figure 2b. Radar image from Columbia, SC 1600 GMT (1100 LT) 8/10/2001

5 Figure 2c. Radar image from Columbia, SC 1900 GMT (1400 LT) 8/10/2001 Figure 2d. GOES-8 IR image, 19GMT (1400 LT) 8/10/2001

6 Figure 3a. Radar and Satellite from 8/10/ GMT (1700 LT)

7 Figure 3b. Radar and Satellite from 8/11/ GMT (8/10/ LT)

8 Figure 3c. Radar and Satellite from 8/11/ GMT (8/10/ LT)

9 Figure 3d. Radar and Satellite from 8/11/ GMT (0200 LT)

10 Figure 4a. Precipitation map for 2200 GMT (1700 LT) 8/10/2001. Values inside the white rectangles show the precipitation in inches for the previous hour.

11 Figure 4b. Precipitation map for 8/11/ GMT (8/10/ LT)

12 Figure 4c. Precipitation map for 8/11/ GMT (8/10/ LT). Precipitation over the previous hour at KCUB (Columbia) was 1.35 inches or 34 mm.

13 Figure 4d. Precipitation map for 8/11/ GMT (0200 LT)

14 study Observing Stations used in this are distributed between the effecting the current observation, using the Durbin-Watson Test. There was a Sandhills region and the coastal region of North Carolina and South Carolina. Locations of these stations were shown in Figure 1 and the names and locations of all these stations are provided in Table 1. Rainfall data recorded from a tropical cyclone was removed as it is not considered a mesoscale convection. The data was checked for quality, and minor amount of autocorrelation in the data, which has a negligible effect on the results. 3.1 Statistical Analysis of the Observations With all of the data from 14 stations separated into day and night amounts for each month, and overall, we performed several Welch s t-tests on any observations that were flagged by the data It tests for the difference the State Climate Office of North Carolina as poor quality were not used between the true mean of two stations or two times, but unlike other t-tests it in this study. Also any of the hourly assumes that the two groups being observations not recording rainfall were discarded since we focus on the actual tested have unequal variances, but like the other t-tests an approximately convection, there was no need to keep normal population distribution. This t observations with no recorded rainfall. The rainfall data was then separated into day and night, assuming day to be 6 a.m. EST to 6 p.m. EST and night to be statistic also assumes the original hypothesized difference to be 0, and tests for the difference having significant difference from 0: 6 p.m. to 6 a.m. EST. At this point the data was also tested for autocorrelation, which is the preceding observation

15 Station ID Station name Latitude Longitude ROCK Upper Coastal Plain (Rockymount, NC) Res. Stn. LAKE (Raleigh, NC) Lake Wheeler Rd Field Lab KFLO (Florence, Florence Airport SC) KCUB (Columbia, Owens Downtown SC) Airport WHIT (Whiteville, Border Belt NC) Tobacco Res. Stn. KMEB (Maxton, NC) Laurinburg-Maxton Airport KOGB Orangeburg (Orangeburg, SC) Municipal Airport KINS (Kinston, NC) Cunningham Research Station CLIN (Clinton, NC) Horticultural Crops Research Stn Table 1. Listing of Weather Stations and their Locations

16 that µ 1 > µ 2 then the value used as t critical t = X X 1 2 s N s + N (1) is the same as in the original alpha table, where µ i is the i th unknown true population mean. t critical is determined by where X i, 2 s i and N i are the i th sample mean, sample variance, and sample size, respectively. There are also degrees of freedom associated with this test. the degree of freedom of each variable (v), and the probability that serves as the level of significance for the test (α). The α value in all tests was The Welch s t-test was performed on the different times (day vs. night) 3.2 Comparison of Model Forecasts v = v v s N s1 s * 1 2 * 2 N v N v = N 1 = N 1 s + N (2-4) However, t critical is also dependent on the test in question. If it is hypothesized that µ 1 < µ 2 then the value used as t critical will be the opposite sign of the value given in the table. If it is hypothesized 2 with Observations The 6 hr archived forecast precipitation data from the North American Mesoscale (NAM) Model was retrieved for the last phase of the project. This data was for the months of June, July and August 2006, with four 6 hr model runs in each day at 12 km resolution. For this portion of the analysis only two of the original nine stations were used. These stations were KCUB (Columbia) and LAKE (Lake Wheeler). To make the forecast validation easier, the observed

17 precipitation data for each day in the same time period was separated into GMT, 1800 GMT and 0000 GMT (0100 LT, 0700 LT, 1300 LT, and 1900 hr. groups which matched the 6 hr LT). Local time (LT) is the Eastern forecast runs. For example all the Standard Time (EST). The statistical observations from 0600 Z to 1200 Z (0100 LT to 0700 LT) were grouped together to match the 0600 Z model run which forecasts total surface precipitation from 0600 Z to 1200 Z. Welch s T-test was then performed for the difference between observed and analysis described above was performed for all these model runs. 4. Discussion of Results 4.1 Statistical Results As expected from the initial analysis there is significant difference between day and night precipitation in forecast precipitation, along with a the Sandhills region. However, the calculation for percent forecast error as follows: diurnal variation in this precipitation also seems to change with each month as shown in Figure 5 for the Sandhills region. While there is a difference Obs. FX PercentError = *100 Obs. (5) between night and day precipitation in the region, the greatest difference shown by ANOVA and Welch s T-tests Where Obs. is the sum of all the were in July and May in the Sandhills. Figure 5 showed the difference between observed precipitation in a given model run, and FX is the sum of all the forecast precipitation in a given model run. The model runs are 0600 GMT, the summed day precipitation and summed night precipitation for each month and over all the different months in the Sandhills. The difference between

18 the summed day precipitation and summed night precipitation for each Precipitation Difference Region Comparison (Day-Night) Precip Difference (in.) May June July August September total Months and Total Figure 5. Precipitation differences - Night precipitation total for each month and overall subtracted from Day precipitation total for each month and overall. Sandhills Precipitation Difference 15 Precip difference (in.) May June July August September ROCK LAKE KFLO KCUB WHIT KMEB KOGB KINS CLIN -15 Month Figure 6. Sandhills inter-station comparison of precipitation difference: Night precipitation total for each month subtracted from Day precipitation total for each month

19 month for all the sites in the Sandhills is shown in Figure 6. The p- values for each T-test performed for both regions together and both regions individually over all the months in the time period is given in Table 2. between observed and forecast precipitation in any of the model runs. Figure 7 shows the percent error of the NAM model at both the sites, and while the error at LAKE appear significant, in terms of T statistic used, it is not 4.2 Forecast Validation significant. Figure 8 shows the sum The 12 km resolution NAM (North American Mesoscale) model used to test for the difference between observed and forecast precipitation did show a strong difference between the two. The p-values from the T-test indicate this especially well with the 19 EST (0Z) model run (Table 3). Specifically for Columbia (KCUB) which lies in the heart of Sandhills, there was a strongly significant difference between observed and forecast precipitation. The T-test showed that the NAM model was under-predicting the observed precipitation at KCUB (Sandhills). However, at LAKE, located at the northern end of the Sandhills region, there was no significant difference difference between observed and forecast precipitation, that is: (14) AmountDifference = Obs. FX Where Obs. and FX are same as what is described for them previously. Of particular interest is the (statistically significant) percent error in the 1900 LT model run for KCUB, and the (not statistically significant) percent error in the 0100 LT model run for the same station. Both of these are model runs forecasting night precipitation, yet the 1900 LT run showed significant difference for KCUB, while the 0100 LT model run did not show any difference for this site. Also at KCUB (Columbia), the model significantly under-predicted

20 Percent Forecast Error Percent Error (%) LAKE KCUB Time Period (EST) Figure 7. NAM Percent Error for each model run: a negative bar represents the model over-predicting and a positive bar represents the model under-predicting Sum Differences, Act.-FX for each model run 5 4 Amount Difference (in.) LAKE KCUB -1-2 Time (EST) Figure 8. Sum Differences between observed and forecast precipitation.

21 Month p-value May June July August September Table 2. The p-values for Day vs. Night precipitation in the Sandhills region for each month Sum Differences Difference (Obs-FX) FX-Forecast Data Significant Difference when p-value<0.05 Obs.-Observed Data LAKE Run Observed FX Obs.-FX Percent Error p- values KCUB Run Observed FX Obs.-FX Percent Error p- values Table 3. Percent Error and p-values

22 the precipitation. We believe that the reason for this under prediction in the Sandhills region could be the inability of the model land use physics to properly represent horizontal gradients in surface turbulent heat fluxes caused by gradients in soil type, between sand and the day time in the absence of any frontal dynamics and forms clouds. After sun sets, cloud radiation interaction essentially appears to be the main process in causing deep convection and the current numerical models are not able to simulate this deep convection clay in this case. This process well. essentially initiates convection during Acknowledgements Funding for this research was provided by the Division of Atmospheric Sciences, National Science Foundation. under the Grants ATM and ATM We thank Ryan Boyles, Aaron Sims, and Mark Brooks of the State Climate Office of North Carolina for providing technical assistance

North Carolina Climate Variations

North Carolina Climate Variations Sethu Raman Professor of Atmospheric and Marine Sciences and State Climatologist State Climate Office of North Carolina North Carolina State University Latest Drought