Advance CrIS Radiance Assimilation in GSI to Improve Forecasts of High-Impact Weather Events

2017 ANNUAL REPORT

Background

Cloud detection is an important step that remains to be the largest source of uncertainty for satellite infrared data assimilation in numerical weather prediction (NWP). If clouds-affected radiances are treated as clear-sky measurements and then assimilated into NWP models, then the analysis fields would be biased and the NWP forecast skills could be significantly degraded. The optically thin cirrus clouds (e.g., optical depths less than 1) are more difficult to detect than thick clouds. Cirrus clouds regularly cover about 20% of the globe. They are optically thin due to a low concentration of ice particles, not lack of a significant amount of large, non-spherical ice crystals. McNally and Watts (2003) developed a cloud detection algorithm for high-spectral-resolution infrared sounders by comparing the difference spectrum between the observations and cloud-free model simulations (i.e., O-B^clear). In the presence of cloud, the O-B^clear difference spectrum would be closest to the B^cloud-B^clear difference spectrum assuming that the simulated clear and cloudy spectra were free of error. However, errors in the simulations of the background clear and cloudy spectra and in the background atmospheric state itself can enter the minimum residual estimate. The infrared semi-transparent clouds (e.g. thin cirrus) are poorly detected from the current GSI quality control process and thus the cloud-affected CrIS radiances could be treated as clear-sky radiances and assimilated wrongly into the operational systems. In contrast to the model-based cloud detection in the GSI system, an observation-based algorithm need to be developed.

Accomplishments

Detection of clouds within certain vertical layers of the atmospheric from satellite infrared instruments is challenging, especially of those optically thin clouds due to their small thermal contrasts to the background. We developed a new method for cloud detection using the Cross-track Infrared Sounder(CrIS) hyperspectral radiances at shortwave (~4.3 m)and longwave (~15 m) CO2 bands. Specifically, CrIS longwave channelsare firstly paired with shortwave channels based on weighting function altitudes and sensitivity to clouds. A linear relationship of brightness temperaturesbetween each paired channel is then derived for predicting the shortwave channel from the longwave channel in clear-sky conditions. A cloud emission and scattering index (CESI) can finally be defined as the difference of the shortwave channel between the clear-sky, regression model predicted and the observed brightness temperatures. Spatial distributions of such derived CESI for several paired channels in the troposphere are examined for a winter storm that occurred in the eastern part of the United States during 22-24 January 2016. It is shown that the CESI values over the storm regions with optically thin cirrus, fog and supercooled water clouds are positively larger than those over optically thick opaque ice and overshooting clouds or in clear-sky conditions. Of particular interest is that an area of fog and water clouds over Gulf of Mexico, which are indicated by the Visible Infrared Imaging Radiometer Suite (VIIRS) day and night band (DNB) observations, is identified by the CESI. The global distributions of CESIs derived from CrIS double CO2 bands with weighting functions peaks located at about 280 hPa and 231 hPa agree well with the distributions of ice cloud optical thickness contained in the AIRS Version 6 data set (Fig. 1).

Figure  1: Spatial distributions of (a) CESI of pair 3 (~321 hPa), (b) AIRS ice cloud optical depth and (c) AIRS cloud toppressure at CrIS ascending node on 1 January 2016.

Planned work        

• Publish the work on ATMS window channels striping noise mitigation in refereed journal
• Implement the striping noise mitigation for all the five years of S-NPP ATMS data of all channels
• J1 ATMS Ku-Bandradio-frequency interference studies  
• J1 ATMS striping noise analysis    
• Absolute calibration/validation for SNPP and J1 ATMS sounding channels by using GPS RO measurements

Publications           

Lin, L., X. Zou and F. Weng, 2017: Combining CrIS double CO2 bands for detecting clouds located in different vertical layers of the atmosphere.J. Geophy. Res., doi: 10.1002/2016JD025505.

Presentations       

The 97^th Annual Meeting of America Meteorology Society, 22-26 January 2017, Seattle, Washington. An oral presentation entitled “Impacts of a New Bias Estimate and a New Cloud Detection Algorithm on CrIS Data Assimilation” by Zou, Weng, Lin and Li at the 13th Annual Symposium on New Generation Operational Environmental Satellite Systems (Post Launch Activities for GOES-R and JPSS)