Comparison of satellite-estimated and model-forecasted rainfall data during a deadly debris-flow event in Zhouqu, Northwest China

The data of several rainfall products, including those estimated from satellite measurements and those forecasted via numerical weather modeling, for a severe debris-flow event in Zhouqu, Northwest China, are compared and analyzed in this paper. The satellite products, including CPC MORPHing technique（CMORPH）, TMPA-RT, and PERSIANN are all near-real-time retrieved with high temporal and spatial resolutions. The numerical weather model used in this paper for precipitation forecasting is WRF. The results show that all three satellite products can basically reproduce the rainfall pattern, distribution, timing, scale, and extreme values of the event, compared with gauge data. Their temporal and spatial correlation coefficients with gauge data are as high as about 0.6, which is statistically significant at 0.01 level. The performance of the forecasted results modeled with different spatial resolutions are not as good as the satellite-estimated results, although their correlation coefficients are still statistically significant at 0.05 level. From the total rainfall and extreme value time series for the domain, it is clear that, from the grid-to-grid perspective, the passive microwave-based CMORPH and TRMM products are more accurate than the infrared-based PERSIANN, while PERSIANN performs very well from the general point of view, especially when considering the whole domain or the whole convective precipitation system. The forecasted data — especially the highest resolution model domain data — are able to represent the total or mean precipitation very well in the research domain, while for extreme values the errors are large. This study suggests that satellite-retrieved and model-forecasted rainfall data are a useful complement to gauge data, especially for areas without gauge stations and areas not covered by weather radars.

Validation of Global Land Surface Satellite (GLASS) Downward Shortwave Radiation Product in the Rugged Surface

The downward shortwave radiation(DSR) is an essential parameter of land surface radiation budget and many land surface models that characterize hydrological,ecological and biogeochemical processes.The new Global LAnd Surface Satellite(GLASS) DSR datasets have been generated recently using multiple satellite data in China.This study investigates the performances of direct comparison approach,which is mostly used for validation of surface insolation retrieved from satellite data over the plain area,and indirect comparison approach,which needs a fine resolution map of DSR as reference,for validation of GLASS DSR product in time-steps of 1 and 3 hours over three Chinese Ecosystem Research Network sites located in the rugged surface.Results suggest that it probably has a large uncertainty to assess GLASS DSR product using the direct comparison method between GLASS surface insolation and field measurements over complex terrain,especially at Mt.Gongga 3,000 m station with root mean square error of 279.04 and 229.06 W/m2in time-steps of 1 and 3 hours,respectively.Further improvement for validation of GLASS DSR product in the rugged surface is suggested by generation of a fine resolution map of surface insolation and comparison of the aggregated fine resolution map with GLASS product in the rugged surface.The validation experience demonstrates that the GLASS DSR algorithm is satisfactory with determination coefficient of 0.83 and root mean square error of 81.91W/m2over three Chinese Ecosystem Research Network sites,although GLASS product overestimates DSR compared to the aggregated fine resolution map of surface insolation.

Contribution of Ground-Based Cloud Observation to Satellite-Based Cloud Discrimination

One of the biggest factors to deteriorate the satellite product quality is cloud coverage. Therefore, cloud masking process is important to improve the quality of various satellite products. However, satellite-based cloud discrimination algorithm has been developing and efficient ground-based cloud observations are necessary to validate satellite-based cloud discrimination. The purpose of this study is to develop the efficient ground-based cloud observation methodology using whole sky camera. This paper deals with methods how to discriminate cloud portions on whole sky image, how to apply the ground-based cloud observation to the validations for satellite products. For the cloud discrimination on whole sky image, we propose SI （sky index） and BI （brightness index） calculated from RGB （red, green and blue） channels. SI shows the extent of the blueness and gray scale and BI indicates the extent of the brightness. Sun, cloud and blue sky portions are divided by SI and BI threshold. As an application of ground-based cloud observation for the validation of satellite products, clouds portions discriminated from whole sky image are projected onto ground surface with map coordinate. We also examine to compare with cloud portions on whole sky images and MODIS （MODerate resolution Imaging Spectroradiometer） image as one of experiments. The proposed ground-based cloud observation method and its extension to satellite-based cloud discrimination should be connected to improve the quality of satellite products.

Assessment of wind products obtained from multiple microwave scatterometers over the China Seas

Sea surface winds （SSWs） are vital to many meteorological and oceanographic applications, especially for regional study of short-range forecasting and Numerical Weather Prediction （NWP） assimilation. Spaceborne seatterometers can provide global ocean surface vector wind products at high spatial resolution. However, given the limited spatial coverage and revisit time for an individual sensor, it is valuable to study improvements of multiple microwave scatterometer observations, including the advanced scatterometer onboard parallel satellites MetOp-A （ASCAT-A） and MetOp-B （ASCAT-B） and microwave scatterometers aboard Oceansat-2 （OSCAT） and HY-2A （HY2-SCAT）. These four scatterometer-derived wind products over the China Seas （0°-40°N, 105°-135°E） were evaluated in terms of spatial coverage, revisit time, bias of wind speed and direction, after comparison with ERA-Interim forecast winds from the European Centre for Medium-Range Weather Forecasts （ECMWF） and spectral analysis of wind components along the satellite track. The results show that spatial coverage of wind data observed by combination of the four sensors over the China Seas is about 92.8% for a 12-h interval at 12：00 and 90.7% at 24：00, respectively. The analysis of revisit time shows that two periods, from 5：30-8：30 UTC and 17：00-21：00 UTC each day, had no observations in the study area. Wind data observed by the four sensors along satellite orbits in one month were compared with ERA-Interim data, indicating that bias of both wind speed and direction varies with wind speed, especially for speeds less than 7 m/s. The bias depends on characteristics of each satellite sensor and its retrieval algorithm for wind vector data. All these results will be important as guidance in choosing the most suitable wind product for applications and for constructing blended SSW products.

Multi-scale validation strategy for satellite albedo products and its uncertainty analysis

Coarse-resolution satellite albedo products are important for climate change and energy balance research because of their capability to characterize the spatiotemporal patterns of land surface parameters at both the regional and global scales. The accuracy of coarse-resolution products is usually assessed via comparison with in situ measurements. The key issue in the comparison of remote sensing observations with in situ measurements is scaling and uncertainty. This paper presents a strategy for validating 1-km-resolution remote sensing albedo products using field measurements and high-resolution remote sensing observations. Field measurements were collected to calibrate the high-resolution(30 m) albedo products derived from HJ-1a/b images. Then, the calibrated high-resolution albedo maps were resampled(i.e., upscaled) to assess the accuracy of the coarse-resolution albedo products. The samples of field measurements and high-resolution pixels are based on an uncertainty analysis. Two types of coarse-resolution albedo datasets, from global land surface satellite(GLASS) and moderate-resolution imaging spectroradiometer(MODIS), are validated over the middle reaches of the Heihe River in China. The results indicate that the upscaled HJ(Huan Jing means environment in Chinese and this refers to a satellite constellation designed for environment and disaster monitoring by China) albedo, which was calibrated using field measurements, can provide accurate reference values for validating coarse-resolution satellite albedo products. However, the uncertainties in the upscaled HJ albedo should be estimated, and pixels with large uncertainties should be excluded from the validation process.