Millimeter-Wave Precipitation Retrievals and Observed-versus-Simulated Radiance Distributions: Sensitivity to Assumptions

Abstract

Brightness temperature histograms observed at 50–191 GHz by the Advanced Microwave Sounding Unit (AMSU) on operational NOAA satellites are shown to be consistent with predictions made using a mesoscale NWP model [the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)] and a radiative transfer model [TBSCAT/F(λ)] for a global set of 122 storms coincident with the AMSU observations. Observable discrepancies between the observed and modeled histograms occurred when 1) snow and graupel mixing ratios were increased more than 15% and 25%, respectively, or their altitudes increased more than ∼25 mb; 2) the density, F(λ), of equivalent Mie-scattering ice spheres increased more than 0.03 g cm−3; and 3) the two-stream ice scattering increased more than ∼1%. Using the same MM5/TBSCAT/F(λ) model, neural networks were developed to retrieve the following from AMSU and geostationary microwave satellites: hydrometeor water paths, 15-min average surface-precipitation rates, and cell-top altitudes, all with 15-km resolution. Simulated AMSU rms precipitation-rate retrieval accuracies ranged from 0.4 to 21 mm h−1 when grouped by octaves of MM5 precipitation rate between 0.1 and 64 mm h−1, and were ∼3.8 mm h−1 for the octave 4–8 mm h−1. AMSU and geostationary microwave (GEM) precipitation-rate retrieval accuracies for random 50–50 mixtures of profiles simulated with either the baseline or a modified-physics model were largely insensitive to changes in model physics that would be clearly evident in AMSU observations if real. This insensitivity of retrieval accuracies to model assumptions implies that MM5/TBSCAT/F(λ) simulations offer a useful test bed for evaluating alternative millimeter-wave satellite designs and methods for retrieval and assimilation, to the extent that surface effects are limited.

The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

“…In the second and Table 1 the training protocol described in has been applied, and for each input configuration (each row in the table) more than 100 NNs (with different levels and nodes) were compared to select the optimal network configuration, where "optimal" refers to the one with best performance, i.e., minimum CV over the full dynamic range of the inputs, absence of overfitting, and absence of anomalous inhomogeneities in the retrievals (Staelin and Surussavadee, 2007).…”

“…NNs have been used in precipitation retrieval -precipitation being one of the most difficult of all atmospheric variables to retrieve -because of the opportunities offered by their ability to learn and generalize (Hsu et al, 1997;Hall et al, 1999;Staelin et al, 1999;Sorooshian et al, 2000;Chen and Staelin, 2003;Hong et al, 2004;Surussavadee and Staelin, 2007, 2008a, 2010Bellerby, 2007;Krasnopolsky et al, 2008;Leslie et al, 2008;Mahesh et al, 2011). However, it should be mentioned that the use of NNs involves the training phase with a large representative database, often obtained from cloud-resolving model simulations.…”

Neural network Jacobian analysis for high-resolution profiling of the atmosphere

“…Neural nets are particularly appealing for the inversion of atmospheric remote sensing data, where relationships are commonly nonlinear and non-Gaussian, and the physical processes may not be well understood. Neural networks were perhaps first applied in the atmospheric remote sensing context by Escobar-Munoz et al [9], and many other investigators have recently reported on the use of neural networks for inversion of microwave sounding observations for the retrieval of temperature and water vapor [8,7][ [10][11][12][13] and hydrologic parameters [14][15][16][17][18][19][20][21][22], as well as inversion of infrared sounding observations for retrieval of temperature and water vapor [23][24][25][26][27] and trace gases [28]. Neural networks have also been used in the geophysical context for nonlinear data representation [29].…”

Detection and Measurement of Snowfall from Space

“…The multi-sensor approach was first based on a logistic distribution to represent the probability of snowfall given the predictors and then using a Bayesian technique. A comparison was carried out with retrievals from the technique of the group at Massachusetts Institute of Technology [79][80][81][82] showing that both proposed methods discriminate snow and no-snow conditions in the polar regions with an overall reduction of the false alarms by at least 30% while considerably increasing the probability of detection. This would confirm the potential of using multisensor, multispectral approaches.…”