Laura J. Bickmeier

Neural network microwave precipitation retrievals and modeling results

We describe a simulation methodology used to develop and validate precipitation retrieval algorithms for current and future passive microwave sounders with emphasis on the NPOESS (National Polar-orbiting Operational Environmental Satellite System) sensors. Precipitation algorithms are currently being developed for ATMS, MIS, and NAST-M. ATMS, like AMSU, will have channels near the oxygen bands throughout 50-60 GHz, the water vapor resonance band at 183.31 GHz, as well as several window channels. ATMS will offer improvements in radiometric and spatial resolution over the AMSU-A/B and MHS sensors currently flying on NASA (Aqua), NOAA (POES) and EUMETSAT (MetOp) satellites. The similarity of ATMS to AMSU-A/B will allow the AMSU-A/B precipitation algorithm developed by Chen and Staelin to be adapted for ATMS, and the improvements of ATMS over AMSU-A/B suggest that a superior precipitation retrieval algorithm can be developed for ATMS. Like the Chen and Staelin algorithm for AMSU-A/B, the algorithm for ATMS to be presented will also be based a statisticsbased approach involving extensive signal processing and neural network estimation in contrast to traditional physics-based approaches. One potential advantage of a neural-network-based algorithm is computational speed. The main difference in applying the Chen-Staelin method to ATMS will consist of using the output of the most up-to-date simulation methodology instead of the ground-based weather radar and earlier versions of the simulation methodology. We also present recent progress on the millimeter-wave radiance simulation methodology that is used to derive simulated global ground-truth data sets for the development of precipitation retrieval algorithms suitable for use on a global scale by spaceborne millimeter-wave spectrometers. The methodology utilizes the MM5 Cloud Resolving Model (CRM), at 1-km resolution, to generate atmospheric thermodynamic quantities (for example, humidity and hydrometeor profiles). These data are then input into a Radiative Transfer Algorithm (RTA) to simulate at-sensor millimeter-wave radiances at a variety of viewing geometries. The simulated radiances are filtered and resampled to match the sensor resolution and orientation.

Photon-Counting Lidar for Aerosol Detection and 3-D Imaging §

Laser-based remote sensing is undergoing a remarkable advance due to novel technologies developed at MIT Lincoln Laboratory. We have conducted recent experiments that have demonstrated the utility of detecting and imaging low-density aerosol clouds. The Mobile Active Imaging LIDAR (MAIL) system uses a Lincoln Laboratory-developed microchip laser to transmit short pulses at 14-16 kHz Pulse Repetition Frequency (PRF), and a Lincoln Laboratory-developed 32x32 Geiger-mode Avalanche-Photodiode Detector (GmAPD) array for singlephoton counting and ranging. The microchip laser is a frequency-doubled passively Q-Switched Nd:YAG laser providing an average transmitted power of less than 64 milli-Watts. When the avalanche photo-diodes are operated in the Geiger-mode, they are reverse-biased above the breakdown voltage for a time that corresponds to the effective range-gate or range-window of interest. The time-of-flight, and therefore range, is determined from the measured laser transmit time and the digital time value from each pixel. The optical intensity of the received pulse is not measured because the GmAPD is saturated by the electron avalanche. Instead, the reflectivity of the scene, or relative density of aerosols in this case, is determined from the temporally and/or spatially analyzed detection statistics.

Improved Simulation Methodology for Retrieval of Convective Precipitation from Spaceborne Passive Microwave Measurements

This manuscript focuses on recent efforts for the development and validation of passive microwave precipitation retrieval algorithms for the NPOESS (National Polar-orbiting Operational Environmental Satellite System) satellite program. Emphasis will be placed on the following three critical components: a methodology for simulating passive microwave observations, a technique for validating the methodology with aircraft measurements, and a statistics-based algorithm for estimating precipitation rate.

Neural network retrieval of precipitation using NPOESS microwave sensors

This paper will present efforts for the development and validation of passive microwave precipitation retrieval algorithms for the NPOESS (national polar-orbiting operational environmental satellite system) satellite program and the NPOESS preparatory project (NPP) prior to the launch of the first satellite in 2009. The advanced technology microwave sounder (ATMS) offers improvements including finer sampling and spatial resolution over heritage instruments such as the advanced microwave sounding unit instruments AMSU-A/B aboard the NOAA-15, NOAA-16, and NOAA-17, and similar instruments. The conical scanning microwave sounder (CSMS) is planned for the second and subsequent NPOESS satellites. A system for simulating ATMS and CSMS microwave observations from atmospheric data has been developed. This system has shown encouraging results when validated with observations from AMSU-B on NOAA-16. This system is flexible and can be used not only with cross-track scanning instruments but also with conically scanning instruments. A neural network was trained to estimate 5.2deg MM5 rain rates from simulated ATMS observations. Encouraging agreement was observed. However, this algorithm is only preliminary and many improvements are in progress.

Radiometric Validation of Microwave Satellite Instruments Using the Npoess Aircraft Sounder Testbed-Microwave (NAST-M) Sensor

This paper outlines the results of two recent efforts to use the NPOESS Aircraft Sounder Testbed-Microwave (NAST-M) airborne sensor to directly validate the microwave radiometers on a number of operational satellites. Radiance differences between the NAST-M sensor and the Advanced Microwave Sounding Unit (AMSU) and the Microwave Humidity Sensor (MHS) were found to be less than 1 K for most channels. Comparison results for ocean underflights of the Aqua, NOAA, and MetOp-A satellites are shown.

Improved modeling and retrieval of convective precipitation from spaceborne passive microwave measurements

This manuscript focuses on recent efforts for the development and validation of passive microwave precipitation retrieval algorithms for the NPOESS (National Polar-Orbiting Operational Environmental Satellite System) satellite program. Emphasis will be placed on the following three critical components: a methodology for simulating passive microwave observations, a technique for validating the methodology with aircraft measurements, and a statistics-based algorithm for estimating precipitation rate.