The Advanced Baseline Imager (ABI), designated to be one of the instruments on a future Geo-stationary Operational Environmental Satellite (GOES) series, will introduce a new era for U.S. geostationary environmental remote sensing. ABI is slated to be launched on GOES-R in 2012 and will be used for a wide range of weather, oceanographic, climate, and environmental applications. ABI will have more spectral bands (16), faster imaging (enabling more geographical areas to be scanned), and higher spatial resolution (2 km in the infrared and 1–0.5 km in the visible) than the current GOES Imager. The purposes of the selected spectral bands are summarized in this paper. There will also be improved performance with regard to radiometrics and image navigation/registration. ABI will improve all current GOES Imager products and introduce a host of new products. New capabilities will include detecting upper-level SO2 plumes, monitoring plant health on a diurnal time scale, inferring cloud-top phase and partic... Abstract The Advanced Baseline Imager (ABI), designated to be one of the instruments on a future Geo-stationary Operational Environmental Satellite (GOES) series, will introduce a new era for U.S. geostationary environmental remote sensing. ABI is slated to be launched on GOES-R in 2012 and will be used for a wide range of weather, oceanographic, climate, and environmental applications. ABI will have more spectral bands (16), faster imaging (enabling more geographical areas to be scanned), and higher spatial resolution (2 km in the infrared and 1–0.5 km in the visible) than the current GOES Imager. The purposes of the selected spectral bands are summarized in this paper. There will also be improved performance with regard to radiometrics and image navigation/registration. ABI will improve all current GOES Imager products and introduce a host of new products. New capabilities will include detecting upper-level SO2 plumes, monitoring plant health on a diurnal time scale, inferring cloud-top phase and partic...

The algorithm for operational retrieval of atmospheric temperature and moisture distribution, total column ozone, and surface skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) longwave infrared radiances is presented. The retrieval algorithm uses clear-sky radiances measured by MODIS over land and ocean for both day and night. The algorithm employs a statistical retrieval with an option for a subsequent nonlinear physical retrieval. The synthetic regression coefficients for the statistical retrieval are derived using a fast radiative transfer model with atmospheric characteristics taken from a dataset of global radiosondes of atmospheric temperature, moisture, and ozone profiles. Evaluation of retrieved total precipitable water vapor (TPW) is performed by a comparison with retrievals from the Geostationary Operational Environmental Satellite (GOES) sounder, radiosonde observations, and data from ground-based instrumentation at the Atmospheric Radiation Measurement (A... Abstract The algorithm for operational retrieval of atmospheric temperature and moisture distribution, total column ozone, and surface skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) longwave infrared radiances is presented. The retrieval algorithm uses clear-sky radiances measured by MODIS over land and ocean for both day and night. The algorithm employs a statistical retrieval with an option for a subsequent nonlinear physical retrieval. The synthetic regression coefficients for the statistical retrieval are derived using a fast radiative transfer model with atmospheric characteristics taken from a dataset of global radiosondes of atmospheric temperature, moisture, and ozone profiles. Evaluation of retrieved total precipitable water vapor (TPW) is performed by a comparison with retrievals from the Geostationary Operational Environmental Satellite (GOES) sounder, radiosonde observations, and data from ground-based instrumentation at the Atmospheric Radiation Measurement (A...

The International Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) Processing Package (IAPP) has been developed to retrieve the atmospheric temperature profile, moisture profile, atmospheric total ozone, and other parameters in both clear and cloudy atmospheres from the ATOVS measurements. The algorithm that retrieves these parameters contains four steps: 1) cloud detection and removal, 2) bias adjustment for ATOVS measurements, 3) regression retrieval processes, and 4) a nonlinear iterative physical retrieval. Nine (3 × 3) adjacent High-Resolution Infrared Sounder (HIRS)/3 spot observations, together with Advanced Microwave Sounding Unit-A observations remapped to the HIRS/3 resolution, are used to retrieve the temperature profile, moisture profile, surface skin temperature, total atmospheric ozone and microwave surface emissivity, and so on. ATOVS profile retrieval results are evaluated by root-mean-square differences with respect to radiosonde observa... Abstract The International Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) Processing Package (IAPP) has been developed to retrieve the atmospheric temperature profile, moisture profile, atmospheric total ozone, and other parameters in both clear and cloudy atmospheres from the ATOVS measurements. The algorithm that retrieves these parameters contains four steps: 1) cloud detection and removal, 2) bias adjustment for ATOVS measurements, 3) regression retrieval processes, and 4) a nonlinear iterative physical retrieval. Nine (3 × 3) adjacent High-Resolution Infrared Sounder (HIRS)/3 spot observations, together with Advanced Microwave Sounding Unit-A observations remapped to the HIRS/3 resolution, are used to retrieve the temperature profile, moisture profile, surface skin temperature, total atmospheric ozone and microwave surface emissivity, and so on. ATOVS profile retrieval results are evaluated by root-mean-square differences with respect to radiosonde observa...

Atmospheric sounding of the vertical changes in temperature and moisture is one of the key contributions from meteorological satellites. The concept of using satellite infrared radiation observations for retrieving atmospheric temperature was first proposed by Jean I. F. King. Lewis D. Kaplan noted that the radiation from different spectral regions are primarily emanating from different atmospheric layers, which can be used to retrieve the atmospheric temperature at different heights in the atmosphere. The United States launched the first meteorological satellite Television Infrared Observation Satellite-1 (TIROS-1) on 1 April 1960, opening a new era of observing the Earth and its atmosphere from space. Since then, hundreds of meteorological satellites have been launched by space agencies, including those in Europe, China, Japan, Russia, India, Korea, and others. With the rapid development of atmospheric sounding technology and radiative transfer models, it became possible to determine the atmospheric state from combined satellite- and ground-based measurements. With advances in computing power, forecast model development, data assimilation, and observing technologies, numerical weather prediction (NWP) has achieved consistently better results and thereby improved the prediction and early warning of severe weather events as well as fostered the initial monitoring of global climate change. The purpose of this paper is to summarize and discuss the development of satellite vertical sounding capability, quantitative profile retrieval theory, and applications of satellite-based atmospheric sounding measurements, with a focus on infrared sounding.

Random forests (RFs), an advanced machine learning (ML) method, was used here to develop a robust and rapid quantitative precipitation estimates (QPEs) algorithm for the new-generation geostationary satellite of Himawari-8. In this algorithm, the global precipitation measurement (GPM) product has been employed to train QPE prediction model. The real-time multiband infrared brightness temperature from Himawari-8, combined with the spatiotemporally matched numerical weather prediction (NWP) data from the global forecast system, have been used as predictor variables for QPE. Among the variables used in RF learning model, total precipitable water and K-index from NWP data have the highest rankings, indicating the importance of atmospheric environment for QPE. To enhance the accuracy of RF models or to optimize model training, a sample-balance technique has been utilized to adjust the ratios of samples in nonprecipitation/precipitation classification and quantitative precipitation regression data sets. Further sensitivity and validation analyses help determine the optimal RF classification and regression models for predicting nonprecipitation/precipitation pixel and rain rate. The selected RF classification model is found to predict precipitation area with an accuracy of 0.87. For predicted QPE product, the mean-absolute-error and root-mean-square error of RF regression model are 0.51 and 2.0 mm/h, respectively. Overall, the RF ML algorithm has a higher detection rate over homogenous ocean surface as compared with over land. Meanwhile, this RF algorithm tends to underestimate rain rate, especially in the presence of heavy rainfall. Despite this, it still produces a reasonable pattern of rainfall area and intensity, which are highly consistent with GPM observations.

Knowledge of cloud properties like cloud top height (CTH) is essential to understand their impact on the earth's radiation budget and on climate change. High spectral resolution measurements from the Atmospheric Infrared Sounder (AIRS) are well suited to reveal valuable information about cloud altitude. The CTH retrievals derived from AIRS single field‐of‐view (FOV) radiance measurements are compared with the operational MODIS (Moderate Resolution Imaging Spectroradiometer) cloud product, and Level 2 products obtained from radar and lidar instruments onboard the EOS (Earth Observing System) CloudSat and the CALIPSO (Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellites. Two cases containing a variety of cloud conditions have been studied, and the strengths/shortcomings of CTH products from infrared (IR) sounder radiances are discussed.