Purpose Statement The purpose of this project was to develop software to compare WRF predicted cloudiness and MODIS cloud image products.

Transcription

1 Comparison of WRF Model Outputs and MODIS Image Products for Cloud Presence: A Case Study REU Student Jessica Beres Advisors: Anupma Prakash, Rudi Gens, Nicole Mölders Abstract The ability to accurately predict cloud presence in the high-latitudes is of importance and necessity for aviation. The Weather Research and Forecasting (WRF) model is designed to predict cloud cover for the mid-latitudes. The purpose of this project was to develop software to assess WRF s ability to predict cloudiness. A case study over a section of central Alaska was performed comparing the WRF model predicted cloudiness to the MODIS satellite imaging cloud mask product. The images for March 13, 2008 were compared using ENVI and ESRI ArcGIS software packages at the accepted MODIS and WRF threshold values. A sensitivity analysis was also performed to determine if these values were appropriate for use in the high-latitudes. It was found that the stated MODIS threshold value of 95% is suitable for use while the WRF cloud water mixing rations and snow mixing rations needed to be combined and set to kg/kg and 10-6 kg/kg accordingly. This case study only suggests that these are acceptable threshold values however more data sets and dates need to be investigated in order to determine appropriate threshold values. Purpose Statement The purpose of this project was to develop software to compare WRF predicted cloudiness and MODIS cloud image products. 1. Introduction The ability to accurately forecast cloudiness is necessary in the fields of aviation and defense. These forecasts come from a combination of numerical weather models which are started using current atmospheric data and implying formulas to predict near future atmospheric conditions. For this study the Weather Researching and Forecasting (WRF; Skamarock et al. 2008) model was used to predict the mixing rations of cloud water, rainwater, cloud-ice, and snow, while the MODIS satellite cloud mask was used as the amount of actual cloud cover. The WRF model (Skamarock et al. 2008) is a numeric mesoscale model that predicts atmospheric conditions. WRF has been developed for mid-latitudes and it has to be evaluated for application at high latitudes. Mölders (2008), for instance, evaluated WRF for a summer episode and found that it has a performance similar to other modern models and to WRF simulations performed for mid-latitudes. Mölders and Kramm (2009) assessed WRF s performance in simulating multi-day winter inversions and found that the accuracy in capturing inversions depends strongly on the surface layer and atmospheric boundary layer schemes used. None of these studies assessed WRF s performance in simulating clouds. This aspect is investigated in the present study. A common way to observe cloud coverage is by viewing a satellite image. The Moderate Resolution Imaging Spectoradiometer (MODIS) aboard the Terra and Aqua satellites provides images of atmospheric conditions. MODIS contains 36 different bands that range from µm with 250, 500, and 1000m spatial resolutions. MODIS

2 produces different level products that can be used to view cloud properties. The MODIS image is a synoptic image of the clouds and does not differentiate between lower and upper level clouds in its final product. (NASA 2009). In recent years, satellite derived cloud cover data have been used to asses model predicted cloudiness (e.g. Mölders et al. 1995, Otkin and Greenwald 2008). In this study, a method is developed to asses WRF s ability to predict cloudiness by comparison of column integrated WRF-predicted mixing ratios of cloud-water, rainwater, cloud-ice, and snow and MODIS derived cloud cover data. 2. Model Setup The WRF model was used in this study. WRF was initialized using the final reanalysis data. This data also served as boundary conditions.. Simulations were performed starting March 13, UTC to March UTC (e.g. Chigullapalli and Mölders 2008). The model domain encompasses the atmosphere over Alaska with 150x110 grid points with a 4km increment on an Arakawa C grid. The model domain is centered on 64.5N, W using a polar projection with true latitudes at 30N and 60N. The USGS land-use and soil data are used. Out of the variety of possible setups the following options were chosen: Resolved scale cloud microphysics is dealt with the five class (vapor, rain, snow, cloud ice, and cloud water) bulk parameterization by Hong et al. (2004), and Hong and Lim (2006). In this project the cloud snow mixing ratio (Qsnow) was added to the cloud-water mixing ratio (Qcloud) over each entire grid column to obtain an accurate depiction of the cloudiness within each respective WRF grid column. The saturation adjustment follows Dudhia (1989) and Hong et al. (1998) in separately treating ice and water saturation processes. It allows super-cooled water (cloud-water rainwater) and solid water (ice, snow) to co-exist and a gradual melting of snow falling below the melting layer. A modified version of the Kain-Fritsch scheme (Kain and Fritsch 1990, Kain and Fritsch Kain 2004) is applied to consider sub grid-scale clouds. It utilizes a simple cloud model with moist updrafts and downdrafts, including the effects of detrainment, entrainment, and relatively simple microphysics. Note that sensitivity studies by Chirgullapalli and Mölders (2008) indicate that for the given synoptic situation cumulus clouds and their parameterization hardly impact the model results. Therefore it does not matter for this study that convective clouds are not considered in determining WRF predicated cloudiness. Long-wave radiation is considered by the RRTM based on Mlawer et al. (1997) which is a spectral-band scheme using the correlated-k method. It considers impacts of water vapor, ozone, CO 2, and trace gases (if present) on long-wave radiation, and accounts for cloud optical depth. Shortwave radiation is dealt with in accord to Chou and Suarez (1994). The scheme has a total of 11 spectral bands and considers diffuse and direct solar radiation components in a two-stream approach. It accounts for scattered and reflected components. Ozone is considered using climatological profiles. Surface layer physics are treated using stability functions from Paulson (1970), Dyer and Hicks (1970), and Webb (1970) to compute surface exchange coefficients for heat, moisture, and momentum. A convective velocity to enhance surface fluxes of heat and moisture is determined in accord with Beljaars (1994). Four stability regimes are considered following Zhang and Anthes (1982). The Yonsei University atmospheric boundary layer

3 (ABL) (Hong et al. 2006) is used to describe the turbulent processes in the ABL. It uses a counter-gradient approach to represent fluxes due to non-local gradients (Hong and Pan 1996) and an explicit treatment of the entrainment layer at the top of the ABL. The top of the ABL is defined using a critical bulk Richardson. 3. MODIS Algorithm The MODIS cloud mask algorithm (Ackerman et al. 1997) creates a level 2 MODIS product at 1km and 250 m spatial resolutions. For this study the 1km resolution was yielded and used. This procedure reduced the amount of error when resampling takes place. Not that resampling was required as WRF uses a 4km resolution. The MODIS algorithm classifies pixels through a series of visible and infrared threshold tests to determine if it observes an unobstructed field-of-view (FOV). The cloud mask is band 5 of the MOD35 Cloud image. The algorithm uses 19 of the spectral bands to run a series of 39 tests to determine a level of confidence in the pixel being clear. The algorithm output gives 4 levels of confidence as; confident clear, probably clear, uncertain/probably cloudy, and cloudy. (Ackerman et al.1998) The series of test run by the algorithm are testing for presence of different variables including; sun glint, land/water, aerosols, shadows, and clouds. The test are run as yes/no with yes = 0 and no = 1. In order to determine confidence for a pixel the average off all the tests are taken and classified as: Q = average of all the tests Confident Clear = Q > 0.99 Probably Clear = 0.95 < Q < 0.99 Uncertain/Probably Cloudy = 0.66 < Q < 0.95 Cloudy = Q < 0.66 Due to the nature of the test and the confidence levels it is likely that MODIS reports a false positive and therefore overestimate the cloud coverage. (Ackerman et al. 1997) The region of interest for this study was a 397,868 km 2 area over central Alaska and the western Yukon Territory of Canada, namely the model domain covered by the WRF simulation. Figure 1 shows the study area projected over the coastline of Alaska. 4. Methods The MODIS images were georeferenced using the ENVI software package by using the latitude and longitude files in geographic lat/long and then converted into NAD UTM N projection. The file was then saved as a GeoTiff and imported into ESRI ArcGIS software package. The region of interest (ROI) was created and was extracted from the original MODIS swath image. The MODIS image used acquired at 2145 UTC on March The image was then resampled from 1 km pixels size to 2 km pixel size to facilitate comparison with the WRF model output (which was generated at 4km grid spacing and later resampled to a 2km pixel size. Instead of using the 4 classes of cloudiness as defined by Ackerman (1998), the MODIS product was reclassified to only two classes: cloudy and clear defining the values as classed as cloudy (1) and as clear (0). This two class classification was required for a simple direct comparison with the WRF model output that was also classified into two classes: cloudy and clear.

4 A WRF image was created from the WRF model output which was in NetCDF format. A program to convert the NetCDF file into a GeoTiff image was created by Dr. Rudi Gens of the Alaskan Satellite Facility. The code clipped out the 22 hour time WRF model output of Marsh 13, 2008 and summed up all of the cloud-water, rainwater, cloudice and snow mixing rations within a grid column to produce the image. Two WRF outputs were used to create the WRF cloud mask, i.e. an image of WRF predicted cloudiness. WRF had predicted the mixing ratios of cloud-water (Qcloud), rainwater (Qrain), cloud-ice (Qice) and snow (Qsnow). The sum of the cloud and hydrometeor mixing ratios (Qcloud+Qrain+Qsnow+Qice) makes up the cloudiness in WRF. Qcloud is taken as an indicator for predicted cloud presences of clouds containing liquid water and Qsnow and Qice are the indicators for snow and ice in the atmosphere. A threshold value for the Qcloud mixing ratio was set at with all grid cells with a value greater than this threshold being considered containing a cloud. The threshold value for Qsnow was set as 10-6 with grid cells with a value greater than it containing snow. This created one image indicating clouds with liquid components and one with clouds containing solid components. Note that mixed phase cloud exist where these images overlapped. Both WRF images were georeferenced in ENVI and exported as a GeoTiff file. The images were then cropped to the ROI in ArcMap and the pixels were resampled from 4 km to 2 km. The Qcloud and Qsnow images were added together and the results were reclassified indicating 0 = clear and 2 = cloud. The reclassified MODIS and WRF images were combined using the equation MODIS WRF =Cloud Presence Agreement. The resulting values of the pixels represent the cloud cover from both images. 0 = No cloud in WRF and MODIS 1 = Cloud in MODIS only -1 = Cloud in WRF and MODIS -2 = Cloud in WRF only The statistics for each classification were calculated and compared. 5. Results The resulting image of the classification is shown in figure 2. The total number of occurrences for each classification was determined and the percentage for each was found. Table 1 contains the number of pixels and the percent of each category out of the total. Figure 3 is a pie chart of the percent of total pixels for each class. The total number of occurrences that MODIS and WRF matched as either cloudy or clear was 64%. MODIS and WRF disagreed on the remaining 36% of the occurrences. From these results it may be concluded that WRF acceptably predicated cloudiness for March 13, 2008 at 2200 UTC. Some of the discrepancies between WRF predicted cloudiness and MODIS cloud cover may be due to the slight time offset and uncertainties in threshold values. A sensitivity analysis was performed to determine if the chosen cloud presences threshold values for both models were appropriate. First the MODIS cloud presence thresholds were changed while the WRF thresholds of Qcloud and Qsnow remained at kg/kg and 10-6 kg/kg respectively. The MODIS thresholds were set at intervals of 5% of the scale that the cloud mask product produced. The MODIS WRF comparison classification was performed at each interval. Table 2 shows the percent, value, and the percent of occurrences in each class. The 95% value was the thresholds taken for the

5 original comparison. Figure 4 is a line graph which shows the change in percent of total occurrences for each classification for each MODIS threshold value. Base on these results the recommended MODIS threshold value of 95% is well appropriate and produces stable results. Next the MODIS cloud presence threshold remained the same at 95% and the WRF Qcloud threshold value was changed. The Qsnow value was not changed because changing the value did not affect the cloud presence obtained. Table 3 contains the percent of total occurrences for the MODIS-WRF comparison classes at 5 different threshold values. The kg/kg value was the threshold used for the original classification of Qcloud. Figure 5 is the line graph of the results from the comparisons at the different Qcloud thresholds. Based on these results the cloud-water mixing ration vale of 10-6 kg/kg that is typically used as an indicator for cloudiness has to be lowered to kg/kg for high latitude applications. The bias and accuracy for each change in threshold value for both MODIS and WRF were calculated. The bias values were calculated by dividing the number of occurrences of Cloud WRF Only by the number of Cloud MODIS Only values. (Mölders 2009) Tables 4 and 5 contain the bias values of the MODIS and WRF threshold test. Figures 6 and 7 are plots of the bias values for the different MODIS and WRF thresholds. If the bias value equals 1 then the number of clouds only shown in WRF equals the number of clouds in MODIS. A bias value of 0 means that MODIS identified all of the WRF predicted clouds. If the bias value is great than 1 then there were more WRF only predicted clouds than MODIS only clouds. The accuracy value was calculated by taking the sum of the Both Clear and Both Cloud classes and dividing them by the total number of pixels. (Mölders 2009) Tables 6 and 7 contain the accuracy values for the different MODIS and WRF thresholds and figures 8 and 9 are plots of the values. An accuracy value of 1 would mean that that all of the pixels were classified as either both clear or both cloudy. The closer the accuracy value is to 1 the larger the agreement between MODIS and WRF, i.e. the better the WRF prediction of cloudiness. A value of 0 would mean that the MODIS and WRF did not both agree on any classification. The closer the value is to 0 the smaller the amount of matching pixels. (Narapusetty and Mölders 2005) 6. Discussion: The purpose of this project was to explore a way to examine the differences between the MODIS cloud mask and the WRF cloudiness prediction. If the combined MODIS WRF image is set as the correct amount of cloud presences then the classifications where the models disagreed can be seen as false data. The MODIS cloud mask product concluded that the image was 63% cloud by pixel count. The WRF model predicted that 73% of the image was cloudy based on grid cell count. Trying to get a model to predict what the observed cloud presences is very difficult (e.g. Mölders et al. 1995, Oktin and Greenwald 2008) and the fact that in this study MODIS and WRF agreed on 64% of the cloud presence can be considered important. Based on this result it can be concluded that the WRF acceptably predicted the cloudiness for March 13, UTC. The threshold values that the comparison for this image was taken at are extremely important to the results. The MODIS threshold of 242 or 95% of the 0-255

6 resolution scale was chosen because in the algorithmic for the cloud mask product a pixel with a value less than 0.95 was considered to contain or possibly contain a cloud. In the WRF model, cloud and hydrometeor mixing ratios values greater than 10-6 kg/kg indicate the presences of the variable. In this study, it was assumed that a value greater than kg/kg but less than 10-6 kg/kg indicates the possibility of the variable. Upon examination of the WRF data when the threshold for Qcloud was set to 10-6 kg/kg there were very few grid columns that met the threshold. The Qcloud value was then set to kg/kg and more cloud presences was noted. Because the WRF model was designed for use at midlatitudes the adjustments of the thresholds for cloud and hydrometeor mixing ratios as an indicator for cloud presence for use in the high latitudes was necessary and needs to be further explored. While more clouds were determined with the Qcloud value set to kg/kg there was still a large difference between the MODIS and WRF images. The mixing ration of snow (Qsnow) was added to the cloud-water mixing ration (Qcloud) as clouds can hold one or both of them. This was acceptable because in March in the ROI there is still a great presence of snow in the atmosphere which makes up clouds. For Qsnow the 10-6 kg/kg threshold was chosen as appropriate because at kg/kg the entire ROI was highlighted. Based on the investigations performed it was decided that the combination of Qcloud at kg/kg and Qsnow at 10-6 kg/kg was the appropriate WRF cloud presence predication for the ROI. Upon analysis of the sensitivity study it was found that when the MODIS thresholds were changed a general pattern was found As the MODIS threshold was lowered the amount of clouds observed by MODIS decreased. In response, the percent of WRF Only and MODIS and WRF Clouds increased. This was due to the decrease in the number of pixels containing MODIS Only clouds. When the Qcloud thresholds were changed it was observed that the accuracy for kg/kg and kg/kg were the same. This suggests, that according to this study, after kg/kg the model predicts the same cloudiness. When the threshold was set at 10-6 kg/kg the WRF Only clouds were 0 because the model greatly under predicted the amount compared to the kg/kg threshold. The shape of the line suggests that an appropriate threshold value for Qcloud could be between kg/kg and kg/kg. However, this needs further investigation with more cases involved. Reviewing the bias and accuracy values for the different MODIS threshold yields the same result as the percent of total data. As the threshold increases, the amount of matching MODIS and WRF cloudiness increases, causing the bias and accuracy to become closer to 1. The 99% value is closer than the 95%; however, the difference is very small. It is still appropriate to use the 95% threshold because it is what the MODIS algorithm sets as the confidence level difference between possibly cloudy and possibly clear. When the WRF mixing ratio threshold values were changed the bias and accuracy levels changed in respect to the differences in amount of WRF cloudiness predicted. The bias and accuracy values for kg/kg and kg/kg were the same. The differences between the bias value for kg/kg and 1, and the kg/kg to 1 were and respectively. Judging from the bias values, a threshold somewhere between and kg/kg would yield an image with best WRF and MODIS clouds matching. However, by looking at the accuracy values and percent of total values this is not true.

7 This same procedure needs to be performed for more dates in order to determine if the trends and findings found in this case study hold true. 7. Conclusion: The predicted cloudiness determined from the WRF model output does not completely match the results produced by the MODIS cloud mask product. However, based on the 64% cloudiness agreement between WRF and MODIS it can be concluded that WRF acceptably predicted the cloudiness for March 13, UTC. The threshold values for the WRF cloud and hydrometeor mixing ratios commonly used in mid-latitudes for indicating cloudiness variables needed to be adjusted to account for the high-latitude study area. The originally chosen threshold values of 95% for MODIS, kg/kg for cloud-water mixing ratio (Qcloud) and kg/kg for snow mixing ratio (Qsnow) were appropriate based on the analysis performed in this study. Based on this result it can be concluded that the WRF acceptably predicted the cloudiness for March 13, UTC. 8. Acknowledgements: Computational support was provided by the Geographic Information Network of Alaska (GINA) and in part by a grant of HPC resources from the Arctic Region Supercomputing Center at the University of Alaska Fairbanks as part of the Department of Defense High Performance Computing Modernization Program. The program to convert the WRF file fro NetCDF to Geotiff was created by Dr. Rudi Gens of the Alaska Satellite Facility. References: Ackerman, S., Strabala, K., Menzel, P., Frey, R., Moeller, C., Gumley, L., Baum, M., Schaaf, C., and Riggs, G., Discriminating Clear-Sky From Cloud With MODIS Algorithm Theoretical Basis Document (MOD35), NASA modis.gsfc.nasa.gov/ Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller, C.C., and Gumley, L. E., 1998: Discriminating clear sky from clouds with MODIS. Journal Geophysical Research, 103, 32,141-32,157 Beljaars, A.C.M., 1994: The parameterization of surface fluxes in large-scale models under free convection, Quart. J. Roy. Meteor. Soc., 121, Chigullapalli, S., Mölders, N., Sensitivity studies using the Weather Research and Forecasting (WRF) model. ARSC report, pp. 15. Chou M.-D., Suarez, M.J., 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo , 3, 85pp. Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46,

8 Dyer, A. J., Hicks, B.B., 1970: Flux-gradient relationships in the constant flux layer, Quart.J. Roy. Meteor. Soc., 96, Hong, S.-Y., Lim, J.-O. J., 2006: The WRF Single-Moment 6-Class Microphysics Scheme (WSM6), J. Korean Meteor. Soc., 42, Hong, S.-Y., and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model, Mon. Wea. Rev., 124, Hong, S.-Y., H.-M. H. Juang, and Q. Zhao, 1998: Implementation of prognostic cloud scheme for a regional spectral model, Mon. Wea. Rev., 126, Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A Revised Approach to Ice Microphysical Processes for the Bulk Parameterization of Clouds and Precipitation, Mon. Wea. Rev., 132, Hong, S.-Y., and Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/ detraining plume model and its application in convective parameterization, J. Atmos. Sci., 47, Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain-Fritcsh scheme, The representation of cumulus convection in numerical models, K. A. Emanuel and D.J. Raymond, Eds., Amer. Meteor. Soc., 246 pp. Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 43, Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long wave. J. Geophys. Res., 102 (D14), Mölders, N., Laube, M., A numerical study on the influence of different cloud treatment in a chemical transport model on gas phase distribution. Atmos. Res. 32, Mölders, N., Laube, M., Raschke, E., Evaluation of model generated cloud cover by means of satellite data. Atmos. Res., 39, Mölders, N., and Kramm, G., 2009: A case study on wintertime inversions in Interior Alaska with WRF. Mölders, N., 2008: Suitability of Weather Research and Forecasting (WRF) Model to Predict the June 2005 Fire Weather for Interior Alaska. Wea. Fore. 23,

9 Narapusetty, B., Mölders, N., Evaluation of snow depth and soil temperature predicted by the Hydro-Thermodynamic Soil Vegetation Scheme (HTSVS) coupled with the Penn State/NCAR Mesoscale Meteorological Model (MM5). J. Appl. Meteor., 44, National Aeronautics and Space Administration. 2009: MODIS Web. Otkin, J.A., Greenwald, T.J., Comparison of WRF model-simulated and MODISderived cloud data. Mon. Wea. Rev. 136, Paulson, C. A., 1970: The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J. Appl. Meteor., 9, Skamarock, W. C., Klemp, J. B., 2008: A time split non hydrostatic atmospheric model for weather research and forecasting applications. J. Comp. Physics., 227, 7, Webb, E. K., 1970: Profile relationships: The log-linear range and extension to strong stability, Quart. J. Roy. Meteor. Soc., 96, Zhang, D.-L., and R.A. Anthes, 1982: A high-resolution model of the planetary boundary layer sensitivity tests and comparisons with SESAME 79 data. J. Appl. Meteor., 21,

10 Figures and Tables are on the following pages.

11 Figure 1: ArcGIS generated image of the region of interest against the Alaskan coastline. The green area indicated the WRF model domain used in this study.

12 Figure 2: MODIS - WRF = Cloud Presence Agreement comparison image at 2km resolution.

13 Output Number of Occurrences Percent of Total Cloud WRF only % Cloud MODIS only % Cloud MODIS and WRF % Clear MODIS and WRF % Table 1: The number of occurrences (pixels) and percentage from total for each classification. 13.7% Percent of Total Occurances 23.2% Cloud WRF only Cloud MODIS only 12.5% Cloud MODIS and WRF 50.6% Clear MODIS and WRF Figure 3: Comparison of the occurrences of each MODIS WRF classification.

14 Threshold Percent MODIS Value Cloud WRF Only Cloud Both Clear Both Cloud MODIS Only 99% % 51.0% 12.3% 13.8% 95% % 50.6% 13.7% 12.5% 90% % 50.0% 12.6% 13.6% 85% % 49.6% 12.7% 13.5% 80% % 49.2% 12.8% 13.3% 75% % 48.7% 12.9% 13.2% 70% % 48.2% 13.1% 13.1% 65% % 47.8% 13.2% 13.0% 60% % 47.4% 13.3% 12.8% 55% % 47.0% 13.5% 12.7% 50% % 46.6% 13.6% 12.6% 45% % 46.1% 13.7% 12.5% 40% % 45.8% 13.8% 12.4% 35% % 45.3% 13.6% 12.5% 30% % 45.0% 14.1% 12.1% 25% % 44.6% 14.2% 12.0% 20% % 44.2% 14.3% 11.9% 15% % 43.8% 14.4% 11.7% 10% % 43.3% 14.5% 11.6% 5% % 42.8% 14.7% 11.5% 1% % 42.3% 14.8% 11.3% Table 2: Percent of total occurrences values for MODIS-WRF comparison with the MODIS cloud mask threshold set at varying values. The WRF mixing ratios remained the same with Qcloud = kg/kg and Qsnow = 10-6 kg/kg. The 95% value was the threshold used for the original comparison of the data.

15 Percent of Total Occurrences 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% MODIS - WRF Comparison at Multiple MODIS Thresholds 99% 85% 70% 55% 40% 25% 10% MODIS Threshold Percent Cloud WRF Only Cloud Both Clear Both Cloud MODIS Only Figure 4: Line graph comparing the MODIS WRF comparison results at varying MODIS thresholds. The four classifications are graphed as percent of total occurrences for each MODIS value. Qcloud Threshold (kg/kg) Cloud WRF Only Cloud Both Clear Both Cloud MODIS Only 1E-6 0.0% 3.7% 47.9% 48.4% 1E % 17.4% 31.9% 46.8% 1E % 28.6% 20.0% 35.7% 1E % 50.6% 12.5% 13.6% 1E % 50.6% 12.4% 13.7% Table 3: Percent of total occurrences for MODIS - WRF comparison results at varying Qcloud thresholds. The MODIS threshold of 95% and the Qsnow values of 10-6 kg/kg both remained the same. The Qcloud threshold of kg/kg was the value used for the original comparison.

16 MODIS - WRF Comparison at Multiple WRF Thresholds 60.0% Percent of Total Occurences 50.0% 40.0% 30.0% 20.0% 10.0% Cloud WRF Only Cloud Both Clear Both Cloud MODIS Only 1E-24 1E-21 1E-18 1E-15 1E-12 1E WRF Qcloud Thresholds 0.0% Figure 5: Logarithmic line graph of MODIS-WRF comparison results at varying WRF Qcloud thresholds. The MODIS threshold of 95% and the Qsnow values of 10-6 kg/kg both remained the same.

17 MODIS Threshold Bias (WRF/MODIS) 99% % % % % % % % % % % % % % % % % % % % % 2.78 Table 4: The bias for the MODIS results were calculated using the Cloud WRF Only and Cloud MODIS Only values.

18 MODIS Multiple Thresholds Results Bias Bias (WRF/MODIS) % 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% MODIS Threshold Value Figure 6: Line graph of the bias results for the MODIS WRF comparison at different MODIS cloud mask thresholds. The values used were the Cloud WRF Only and Cloud MODIS Only for each threshold. Qcloud Threshold kg/kg Bias (WRF/MODIS) 1E-6 0 1E E E E Table 5: The bias results for the different WRF thresholds were calculated using the Cloud WRF Only and Cloud MODIS Only values.

19 WRF Multiple Qcloud Thresholds Accuracy Values Accuracy E-24 1E-21 1E-18 1E-15 1E WRF Qcloud Threshold Figure 7: Logarithmic plot showing the bias values the different WRF thresholds were calculated using the Cloud WRF Only and Cloud MODIS Only values.

20 Table 6: MODIS Threshold Accuracy 99% % % % % % % % % % % % % % % % % % % % % Table 6: Accuracy values for the different MODIS thresholds were calculated by taking the sum of the Cloud Both and Clear Both classes and divided by the sum of all four classes.

21 MODIS Multiple Thresholds Acucuracy Values Accuracy % 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% MODIS Threshold Figure 8: Graph of accuracy values for the different MODIS thresholds that were calculated by taking the sum of the Cloud Both and Clear Both classes and divided by the sum of all four classes. Qcloud Threshold (kg/kg) Accuracy 1E E E E E Table 7: Accuracy values for the different WRF Qcloud thresholds were calculated by taking the sum of the Cloud Both and Clear Both classes and divided by the sum of all four classes

22 WRF Multiple Qcloud Thresholds Accuracy Values Accuracy E-24 1E-21 1E-18 1E-15 1E WRF Qcloud Threshold Figure 9: Plot of the accuracy values for the different WRF Qcloud thresholds were calculated by taking the sum of the Cloud Both and Clear Both classes and divided by the sum of all four classes.