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  • 12th International Precipitation Conference (IPC12) x
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Clement Guilloteau, Efi Foufoula-Georgiou, Pierre Kirstetter, Jackson Tan, and George J. Huffman

Abstract

As more global satellite-derived precipitation products become available, it is imperative to evaluate them more carefully for providing guidance as to how well precipitation space–time features are captured for use in hydrologic modeling, climate studies, and other applications. Here we propose a space–time Fourier spectral analysis and define a suite of metrics that evaluate the spatial organization of storm systems, the propagation speed and direction of precipitation features, and the space–time scales at which a satellite product reproduces the variability of a reference “ground-truth” product (“effective resolution”). We demonstrate how the methodology relates to our physical intuition using the case study of a storm system with rich space–time structure. We then evaluate five high-resolution multisatellite products (CMORPH, GSMaP, IMERG-Early, IMERG-Final, and PERSIANN-CCS) over a period of 2 years over the southeastern United States. All five satellite products show generally consistent space–time power spectral density when compared to a reference ground gauge–radar dataset (GV-MRMS), revealing agreement in terms of average morphology and dynamics of precipitation systems. However, a deficit of spectral power at wavelengths shorter than 200 km and periods shorter than 4 h reveals that all satellite products are excessively “smooth.” The products also show low levels of spectral coherence with the gauge–radar reference at these fine scales, revealing discrepancies in capturing the location and timing of precipitation features. From the space–time spectral coherence, the IMERG-Final product shows superior ability in resolving the space–time dynamics of precipitation down to 200-km and 4-h scales compared to the other products.

Open access
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

Abstract

This paper describes a new Passive Microwave Empirical Cold Surface Classification Algorithm (PESCA) developed for snow-cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, since several studies have highlighted the influence of snow-cover radiative properties on the falling-snow passive microwave signature. The developed method is based on the exploitation of the lower-frequency channels (<90 GHz), common to most microwave radiometers. The method applied to the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the cross-track-scanning Advanced Technology Microwave Sounder (ATMS) is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust both for sensors in dry conditions (total precipitable water < 10 mm) and for mean surface elevation < 2500 m, independent of the cloud cover. The algorithm shows very good performance for cold temperatures (2-m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA (overall detection statistics: probability of detection is 0.98 for ATMS and 0.92 for GMI, false alarm ratio is 0.01 for ATMS and 0.08 for GMI, and Heidke skill score is 0.72 for ATMS and 0.69 for GMI). Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometries, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes also appear to be different at high frequency (>90 GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically scanning and cross-track-scanning radiometers, including the future operational EUMETSAT Polar System Second Generation (EPS-SG) mission microwave radiometers.

Open access
Clément Guilloteau and Efi Foufoula-Georgiou

Abstract

The quantitative estimation of precipitation from orbiting passive microwave imagers has been performed for more than 30 years. The development of retrieval methods consists of establishing physical or statistical relationships between the brightness temperatures (TBs) measured at frequencies between 5 and 200 GHz and precipitation. Until now, these relationships have essentially been established at the “pixel” level, associating the average precipitation rate inside a predefined area (the pixel) to the collocated multispectral radiometric measurement. This approach considers each pixel as an independent realization of a process and ignores the fact that precipitation is a dynamic variable with rich multiscale spatial and temporal organization. Here we propose to look beyond the pixel values of the TBs and show that useful information for precipitation retrieval can be derived from the variations of the observed TBs in a spatial neighborhood around the pixel of interest. We also show that considering neighboring information allows us to better handle the complex observation geometry of conical-scanning microwave imagers, involving frequency-dependent beamwidths, overlapping fields of view, and large Earth incidence angles. Using spatial convolution filters, we compute “nonlocal” radiometric parameters sensitive to spatial patterns and scale-dependent structures of the TB fields, which are the “geometric signatures” of specific precipitation structures such as convective cells. We demonstrate that using nonlocal radiometric parameters to enrich the spectral information associated to each pixel allows for reduced retrieval uncertainty (reduction of 6%–11% of the mean absolute retrieval error) in a simple k-nearest neighbors retrieval scheme.

Open access
Stephen E. Lang and Wei-Kuo Tao

Abstract

The Goddard convective–stratiform heating (CSH) algorithm, used to estimate cloud heating in support of the Tropical Rainfall Measuring Mission (TRMM), is upgraded in support of the Global Precipitation Measurement (GPM) mission. The algorithm’s lookup tables (LUTs) are revised using new and additional cloud-resolving model (CRM) simulations from the Goddard Cumulus Ensemble (GCE) model, producing smoother heating patterns that span a wider range of intensities because of the increased sampling and finer GPM product grid. Low-level stratiform cooling rates are reduced in the land LUTs for a given rain intensity because of the rain evaporation correction in the new four-class ice (4ICE) scheme. Additional criteria, namely, echo-top heights and low-level reflectivity gradients, are tested for the selection of heating profiles. Those resulting LUTs show greater and more precise variation in their depth of heating as well as a tendency for stronger cooling and heating rates when low-level dBZ values decrease toward the surface. Comparisons versus TRMM for a 3-month period show much more low-level heating in the GPM retrievals because of increased detection of shallow convection, while upper-level heating patterns remain similar. The use of echo tops and low-level reflectivity gradients greatly reduces midlevel heating from ~2 to 5 km in the mean GPM heating profile, resulting in a more top-heavy profile like TRMM versus a more bottom-heavy profile with much more midlevel heating. Integrated latent heating rates are much better balanced versus surface rainfall for the GPM retrievals using the additional selection criteria with an overall bias of +4.3%.

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