Optimal Use of Space-Borne Advanced Infrared and Microwave Soundings for Regional Numerical Weather Prediction

Abstract

Satellite observations can either be assimilated as radiances or as retrieved physical parameters to reduce error in the initial conditions used by the Numerical Weather Prediction (NWP) model. Assimilation of radiances requires a radiative transfer model to convert atmospheric state in model space to that in radiance space, thus requiring a lot of computational resources especially for hyperspectral instruments with thousands of channels. On the other hand, assimilating the retrieved physical parameters is computationally more efficient as they are already in thermodynamic states, which can be compared with NWP model outputs through the objective analysis scheme. A microwave (MW) sounder and an infrared (IR) sounder have their respective observational limitation due to the characteristics of adopted spectra. The MW sounder observes at much larger field-of-view (FOV) compared to an IR sounder. On the other hand, MW has the capability to reveal the atmospheric sounding when the clouds are presented, but IR observations are highly sensitive to clouds, The advanced IR sounder is able to reduce uncertainties in the retrieved atmospheric temperature and moisture profiles due to its higher spectral-resolution than the MW sounder which has much broader spectra bands. This study tries to quantify the optimal use of soundings retrieved from the microwave sounder AMSU and infrared sounder AIRS onboard the AQUA satellite in the regional Weather and Research Forecasting (WRF) model through three-dimensional variational (3D-var) data assimilation scheme. Four experiments are conducted by assimilating soundings from: (1) clear AIRS single field-of-view (SFOV); (2) retrieved from using clear AMSU and AIRS observations at AMSU field-of-view (SUP); (3) all SFOV soundings within AMSU FOVs must be clear; and (4) SUP soundings which must have all clear SFOV soundings within the AMSU FOV. A baseline experiment assimilating only conventional data is generated for comparison. Various atmospheric state variables at different pressure levels are used to assess the impact from assimilating these different data by comparing them with European Centre for Medium Range Weather Forecast (ECMWF) reanalysis data. Results indicate assimilation of SUP soundings improve the mid and upper troposphere, whereas assimilation of SFOV soundings has positive impact on the lower troposphere. Two additional assimilation experiments are carried out to determine the combination of SUP and SFOV soundings that will provide the best performance throughout the troposphere. The results indicate that optimal combination is to assimilate clear-sky matched IR retrievals with non-matched MW soundings.

An Analysis Study of FORMOSAT-7/COSMIC-2 Radio Occultation Data in the Troposphere

“…The sound profiles contain more than 100 pressure levels from surface to the 0.005 hPa, with uncertainties of about 1 K and 20% in temperature and moisture mixing ratio in the troposphere from the NUCAPS Algorithm Theoretical Basis Document (ATBD). These data have been supported for both operational applications and research studies (e.g., [21][22][23]).…”

Objective Evaluation of Satellite Precipitation Datasets for Heavy Precipitation Events Caused by Typhoons in the Philippines

“…The temporal variation in rainfall is a critical factor in the assessment of extreme weather phenomena and the hydrological cycle (Liu et al 2016a;Liu et al 2016b;Huang et al 2019). Heavy rainfall in a short period can cause natural disasters, such as floods and landslides.…”

Comparison of Cloud‐Top Property Retrievals From Advanced Himawari Imager, MODIS, CloudSat/CPR, CALIPSO/CALIOP, and Radiosonde

“…Community Satellite Processing Package for geostationary satellite (CSPP‐GEO) package has been developed at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at University of Wisconsin‐Madison and is able to provide not only calibrated and geolocated sensor observation but also cloud retrievals from raw satellite telemetry (Martin et al, 2016) as a research algorithm. With the localized lookup tables (LUTs) for AHI's spectral bands and the use of high‐resolution atmospheric thermodynamic states (Liu, Kuo, et al, 2016) at 3‐km horizontal grid resolution, the cloud products are inferred from ABI with CSPP‐GEO and hereafter referred to as AHI retrieval. The retrieval is a result of sophisticated procedure, based on calculation of Planck thermal radiation LUTs for pixel by pixel of latitudes, longitudes, sensor azimuth/zenith angles, land/sea mask, and ecosystem type for surface IR emissivity/shortwave albedo as well as elevation.…”