The 183-WSL fast rain rate retrieval algorithm

Laviola, Sante; Levizzani, Vincenzo

doi:10.1016/j.atmosres.2010.11.013

Cited by 61 publications

(39 citation statements)

References 94 publications

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“…It is important to note here that there is no attempt to determine a rain rate as proposed by other authors (e.g. Kongoli et al, 2007;Vila et al, 2007;Surussawadee and Staelin, 2009;Di Tomaso et al, 2009;Laviola and Levizzani, 2011). The weak correlation of scattering intensity and instantaneous rain rate at the surface (e.g.…”

Section: Dataset Propertiesmentioning

confidence: 99%

“…Specifically, the satellite data provided by the Advanced Microwave Sounding Unit (AMSU) radiometer allow to detect moderate and heavy precipitating areas (e.g. Kongoli et al, 2007;Vila et al, 2007;Surussawadee and Staelin, 2009;Di Tomaso et al, 2009;Laviola and Levizzani, 2011). This takes advantage of the fact that AMSU observations (a) are available on several platforms (five NOAA platforms and Metop-2) and thus offer a good temporal sampling, (b) allow a screening of precipitation over the whole of the Mediterranean Basin, including the sea where in-situ observations are scarce, and (c) have a fine spatial resolution (16 km at nadir).…”

Section: Claud Et Al: a High Resolution Climatology Of Precipitatmentioning

confidence: 99%

“…Fig. 2 of Laviola and Levizzani, 2011). Over such areas, we use an additional constraint that a precipitating region must attain an upper-tropospheric humidity (UTH) relative to ice of at least 70 %.…”

Section: Moderate Rain and Deep Convection Diagnosticsmentioning

confidence: 99%

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A high resolution climatology of precipitation and deep convection over the Mediterranean region from operational satellite microwave data: development and application to the evaluation of model uncertainties

Claud

Alhammoud

Funatsu

et al. 2012

Nat. Hazards Earth Syst. Sci.

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Abstract.A new precipitation and convection dataset for the Mediterranean Basin, derived from operational satellite microwave data is documented. The dataset is derived from diagnostics that rely on brightness temperatures measured since 1999 in the water vapour absorption line (183)(184)(185)(186)(187)(188)(189)(190)(191). The dataset consists of twice-daily (a.m. and p.m.) and monthly maps of precipitation and convection occurrences on a 0.2 • lat × 0.2 • long grid for the area 25 • -60 • N, 10 • W-50 • E. The instruments used so far are the AMSU-B sensor on the NOAA-15 to -17 satellites, and the MHS sensor on the NOAA-18 and -19 and METOP-2 satellites, with precipitation and convection available separately for the different sensors. The slightly different radiometric characteristics of MHS compared to AMSU-B do not affect significantly the continuity of the dataset. Precipitation and convection data from different sensors on different satellites are remarkably consistent, with generally small biases between the instruments. When larger biases appear, they can be explained either by the drifts in the satellite orbit, scan asymmetry, or temporal aliasing from insufficiently resolving the diurnal cycle of precipitation and convection. After a description of climatological aspects of rain and deep convection occurrence, the interest of this dataset to evaluate model uncertainties for simulating a high-impact weather event and for climatic regional runs over this area is illustrated.

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Section: Dataset Propertiesmentioning

confidence: 99%

Section: Claud Et Al: a High Resolution Climatology Of Precipitatmentioning

confidence: 99%

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A high resolution climatology of precipitation and deep convection over the Mediterranean region from operational satellite microwave data: development and application to the evaluation of model uncertainties

Claud

Alhammoud

Funatsu

et al. 2012

Nat. Hazards Earth Syst. Sci.

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“…However, these channels have shown great potential for precipitating cloud characterization and for precipitation retrieval. The different penetration ability of these channels in the atmosphere can be exploited to analyze the vertical distribution of hydrometeors (Wang et al, 1989(Wang et al, , 1997Burns et al, 1997;Staelin and Chen, 2000;Ferraro et al, 2005;Hong et al, 2005Hong et al, , 2008Funatsu et al, 2007Funatsu et al, , 2009Laviola and Levizzani, 2011) and to obtain some criteria for the characterization of precipitation as weak, moderate, strong convective, or stratiform using the TB differences 17 , 13 , and 37 (corresponding, respectively, to the differences between the 183.31 ± 1 and 183.31 ± 7 GHz, 183.31 ± 1 and 183.31 ± 3 GHz, 183.31 ± 3 and 183.31 ± 7 GHz channels) (e.g., Ferraro, 2004;Qiu et al, 2005). In the design of PNPR v2 we have focused on the exploitation of the improved technical characteristics of ATMS with respect to AMSU/MHS, with the analysis of the information carried by two new channels in the 183 GHz water vapor absorption band (at 183.31 ± 4.5 and 183.31 ± 1.8 GHz) (see Sect.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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Abstract. The objective of this paper is to describe the development and evaluate the performance of a completely new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track Advanced Technology Microwave Sounder (ATMS) radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitationsensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered. The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas.The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area the statistical analysis was carried out for a 2-year (2013-2014) dataset of coincident observations over a regular grid at 0.5 • × 0.5 • resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h −1 over ocean and 1.11 mm h −1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h −1 over vegetated land and 0.71 mm h −1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with generally better estimation of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

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“…All of the reflectivity-precipitation rate relationships (Z e − R) fall in the range of previously established relationships for snowfall. A snowfall and snow cover retrieval algorithm [55] is currently being developed as a module of the precipitation retrieval algorithm Water Strong Lines at 183 GHz (183-WSL) of Laviola and Levizzani [56][57][58]. The module is based on TB differences of AMSU-B calibrated against NEXRAD data.…”

Section: Passive Microwave Methodsmentioning

confidence: 99%

Detection and Measurement of Snowfall from Space

2011

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Snowfall detection and measurement represent highly difficult problems in modern hydrometeorology. Ground measurements are complicated due to detection technology limitations, snow drift and accumulation issues, and error definition. The snowfall detection from space is in turn affected by all detection limitations that characterize the measurement of rainfall with the addition of several complications, such as the indirect character of remote sensing precipitation estimation, the presence of frozen or snow-covered terrain, and the unknown vertical distribution of hydrometeors in the cloud column. Several methods for the retrieval of snowfall intensity from satellite have been proposed in recent times using passive and active sensors. No satisfactory answer to the general problem of quantitative snowfall intensity determination has been found to date, but several studies contribute to delineate a working framework for the future operational retrieval algorithms.

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The 183-WSL fast rain rate retrieval algorithm

Cited by 61 publications

References 94 publications

A high resolution climatology of precipitation and deep convection over the Mediterranean region from operational satellite microwave data: development and application to the evaluation of model uncertainties

A high resolution climatology of precipitation and deep convection over the Mediterranean region from operational satellite microwave data: development and application to the evaluation of model uncertainties

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

Detection and Measurement of Snowfall from Space

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