As a microwave radiometer seeing through clouds, Advanced Technology Microwave Sounder (ATMS) observations play a critical role in visually monitoring hurricane warm core structures. However, the presence of orbit gaps within the ATMS observations at low latitudes regions, where hurricanes frequently develop, raises concerns in monitoring hurricanes' spatial variability. To resolve this issue, this study generates gap-filled ATMS brightness temperature data by using the smoothing algorithm of Penalized Least Square Discrete Cosine Transform. The accuracies of the missing-filled brightness temperatures for temperature sounding channels are approximately within 1 K. Furthermore, the gap-filled brightness temperature data from channels 5-12 are utilized to establish a three-dimensional Hurricane Warm Core Animation System (HWCAS) in near real time (NRT), which helps to visually observe realistic warm core structures of a hurricane system. The information of hurricane warm core over open oceans and coastal areas is derived using a combination of three new regression-based atmospheric temperature retrieval algorithms, with the averaged error typically within ±1 K at the vertical levels a warm core could occur. Each animation consists of 97 two-dimensional atmospheric temperature anomaly images at different cross-sections through hurricane core regions. The retrieved maximum temperature anomalies show well the formation, intensification, weakening, reintensification, and dissipation stages of Hurricane Florence that are similar to those from the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis. They also show similar weakening and reintensifying stages to those of the maximum sustained winds by the best track data, albeit with some temporal lead/lag. With its strength in NRT hurricane monitoring, the HWCAS demonstrate its great potential in providing meteorologists with timely information of temperature anomaly fields in the inner-core regions of a hurricane.

No abstract

Two existing double-difference (DD) methods, using either a 3rdSensor or Radiative Transfer Modeling (RTM) as a transfer, are applicable primarily for limited regions and channels, and, thus critical in capturing inter-sensor calibration radiometric bias features. A supplementary method is also desirable for estimating inter-sensor calibration biases at the window and lower sounding channels where the DD methods have non-negligible errors. In this study, using the Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System (JPSS)-1 (alias NOAA-20) as an example, we present a new inter-sensor bias statistical method by calculating 32-day averaged differences (32D-AD) of radiometric measurements between the same instrument onboard two satellites. In the new method, a quality control (QC) scheme using one-sigma (for radiance difference), or two-sigma (for radiance) thresholds are established to remove outliers that are significantly affected by diurnal biases within the 32-day temporal coverage. The performance of the method is assessed by applying it to estimate inter-sensor calibration radiometric biases for four instruments onboard SNPP and NOAA-20, i.e., Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS), Nadir Profiler (NP) within the Ozone Mapping and Profiler Suite (OMPS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Our analyses indicate that the globally-averaged inter-sensor differences using the 32D-AD method agree with those using the existing DD methods for available channels, with margins partially due to remaining diurnal errors. In addition, the new method shows its capability in assessing zonal mean features of inter-sensor calibration biases at upper sounding channels. It also detects the solar intrusion anomaly occurring on NOAA-20 OMPS NP at wavelengths below 300 nm over the Northern Hemisphere. Currently, the new method is being operationally adopted to monitor the long-term trends of (globally-averaged) inter-sensor calibration radiometric biases at all channels for the above sensors in the Integrated Calibration/Validation System (ICVS). It is valuable in demonstrating the quality consistencies of the SDR data at the four instruments between SNPP and NOAA-20 in long-term statistics. The methodology is also applicable for other POES cross-sensor calibration bias assessments with minor changes.

Among the monitored telemetry raw data record (RDR) parameters with the STAR Integrated/Validation System (ICVS), the Advanced Technology Microwave Sounder (ATMS) scan motor mechanism temperature is especially important because the instrument might be unavoidably damaged if the mechanism temperature exceeds 50 °C. In the current operational flight processing software, the instrument automatically enters safe mode and stops collecting scientific data whenever the mechanism temperature exceeds 40 °C. This approach inevitably leads to the instrument entering safe mode unnecessarily at a premature time, causing the loss of scientific data before the mechanism temperature reaches 50 °C. This study seeks to leverage the influence the main motor current, compensation motor current, and main motor loop integral error have on mechanism temperature to forecast the maximum mechanism temperature over the upcoming 6 min. A long short-term memory (LSTM) neural network predicts maximum mechanism temperature using ATMS RDR telemetry data as the input. The performance of the LSTM is compared with observed maximum mechanism temperatures by applying the LSTM coefficients to several cases. In all cases studied, the mean average error (MAE) of the forecast remained under 1.1 °C, and the correlation between forecasts and measurements remained above 0.96. These forecasts of maximum mechanism temperature are expected to be able to provide information on when the ATMS instrument should enter safe mode without needlessly losing valuable data for the ATMS flight operational team.