scholarly journals Application of lightning to passive microwave convective and stratiform partitioning in passive microwave rainfall retrieval algorithm over land from TRMM

2012 ◽  
Vol 117 (D23) ◽  
pp. n/a-n/a ◽  
Author(s):  
Nai-Yu Wang ◽  
Kaushik Gopalan ◽  
Rachel I. Albrecht
Keyword(s):  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Rainfall Retrieval

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer

2018 ◽  
Vol 10 (7) ◽  
pp. 1122 ◽  
Author(s):  
Paolo Sanò ◽  
Giulia Panegrossi ◽  
Daniele Casella ◽  
Anna Marra ◽  
Leo D’Adderio ◽  
...  
Keyword(s):  
Neural Network ◽  
Selection Procedure ◽  
Rainfall Rate ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Input Selection ◽  
Channel Response ◽  
Observational Database ◽  
Microwave Imager ◽  
Rainfall Retrieval

This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC > 0.90, ME < −0.22 mm h−1, RMSE < 2.75 mm h−1 and FSE% < 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications.

scholarly journals A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data

2021 ◽  
Vol 11 (10) ◽  
pp. 4686
Author(s):  
Massimiliano Sist ◽  
Giovanni Schiavon ◽  
Fabio Del Frate
Keyword(s):  
Data Fusion ◽  
Low Earth Orbit ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Earth Orbit ◽  
Test Cases ◽  
Orbit Satellite ◽  
Infrared Imager ◽  
Rainfall Retrieval ◽  
Infrared Data

A new data fusion technique based on Artificial Neural Networks (ANN) for the design of a rainfall retrieval algorithm is presented. The use of both VIS/IR (VISible and InfraRed) data from GEO (Geostationary Earth Orbit) satellite and of passive microwave data from LEO (Low Earth Orbit) satellite can take advantage of both types of sensors reducing their limitations. The technique can reconstruct the surface rain field with the MSG-SEVIRI (Meteosat Second Generation–Spinning Enhanced Visible Infrared Imager) spatial and temporal resolution, which means 3 km at the sub satellite point and 5 km at mid-latitudes, every 15 min, respectively. Rainfall estimations are also compared with H-SAF (Hydrology Satellite Application Facility) PR-OBS3A operational product showing better performance both on the identification of rainy areas and on the retrieval of the amount of precipitation. In particular, in the considered test cases, results report an improvement in average of 83% in terms of probability of rainy areas detection, of 45% in terms of false alarm rate, and of 47% in terms of root mean square error in the retrieval of the amount of precipitation.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals A multi-sensor data-driven methodology for all-sky passive microwave inundation retrieval

2017 ◽  
Vol 21 (6) ◽  
pp. 2685-2700 ◽  
Author(s):  
Zeinab Takbiri ◽  
Ardeshir M. Ebtehaj ◽  
Efi Foufoula-Georgiou
Keyword(s):  
Rank Correlation ◽  
Nearest Neighbor Search ◽  
Water Levels ◽  
Passive Microwave ◽  
Sensor Data ◽  
Convective Precipitation ◽  
Retrieval Algorithm ◽  
Inversion Method ◽  
Diurnal Variability ◽  
Microwave Imager

Abstract. We present a multi-sensor Bayesian passive microwave retrieval algorithm for flood inundation mapping at high spatial and temporal resolutions. The algorithm takes advantage of observations from multiple sensors in optical, short-infrared, and microwave bands, thereby allowing for detection and mapping of the sub-pixel fraction of inundated areas under almost all-sky conditions. The method relies on a nearest-neighbor search and a modern sparsity-promoting inversion method that make use of an a priori dataset in the form of two joint dictionaries. These dictionaries contain almost overlapping observations by the Special Sensor Microwave Imager and Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F17 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua and Terra satellites. Evaluation of the retrieval algorithm over the Mekong Delta shows that it is capable of capturing to a good degree the inundation diurnal variability due to localized convective precipitation. At longer timescales, the results demonstrate consistency with the ground-based water level observations, denoting that the method is properly capturing inundation seasonal patterns in response to regional monsoonal rain. The calculated Euclidean distance, rank-correlation, and also copula quantile analysis demonstrate a good agreement between the outputs of the algorithm and the observed water levels at monthly and daily timescales. The current inundation products are at a resolution of 12.5 km and taken twice per day, but a higher resolution (order of 5 km and every 3 h) can be achieved using the same algorithm with the dictionary populated by the Global Precipitation Mission (GPM) Microwave Imager (GMI) products.

A scattering-based over-land rainfall retrieval algorithm for South Korea using GCOM-W1/AMSR-2 data

2017 ◽  
Vol 53 (3) ◽  
pp. 385-392 ◽  
Author(s):  
Young-Joo Kwon ◽  
Hayan Shin ◽  
Hyunju Ban ◽  
Yang-Won Lee ◽  
Kyung-Ae Park ◽  
...  
Keyword(s):  
South Korea ◽  
Retrieval Algorithm ◽  
Rainfall Retrieval

Development of precipitation retrieval algorithm for passive microwave sounder over land

2012 ◽  
Author(s):  
S. Kida ◽  
T. Kubota ◽  
M. Kachi ◽  
S. Shige ◽  
R. Oki
Keyword(s):  
Passive Microwave ◽  
Retrieval Algorithm

scholarly journals Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1225
Author(s):  
Lanka Karthikeyan ◽  
Ming Pan ◽  
Dasika Nagesh Kumar ◽  
Eric F. Wood
Keyword(s):  
Soil Moisture ◽  
Global Scale ◽  
Passive Microwave ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Structural Uncertainty ◽  
Three Solutions ◽  
Brightness Temperatures ◽  
Retrieval Algorithms

Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.

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