scholarly journals The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies

2014 ◽  
Vol 7 (9) ◽  
pp. 9351-9411 ◽  
Author(s):  
P. Sanò ◽  
G. Panegrossi ◽  
D. Casella ◽  
F. Di Paola ◽  
L. Milani ◽  
...  
Keyword(s):  
Neural Network ◽  
Case Studies ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Passive Microwave ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Precipitation Pattern ◽  
Neural Network Approach ◽  
Cross Track

Abstract. The purpose of this study is to describe a new algorithm based on a Neural Network approach (Passive microwave Neural network Precipitation Retrieval – PNPR) for precipitation rate estimation from AMSU/MHS observations, and to provide examples of its performance for specific case studies over the European/Mediterranean area. The algorithm optimally exploits the different characteristics of AMSU-A and MHS channels, and their combinations, including the TB differences of the 183.31 channels, with the goal of having a single neural network for different types of background surfaces (vegetated land, snow covered surface, coast and ocean). The training of the neural network is based on the use of a cloud-radiation database, built from cloud-resolving model simulations coupled to a radiative transfer model, representative of the European and Mediterranean basin precipitation climatology. The algorithm provides also the phase of the precipitation and a pixel-based confidence index for the evaluation of the reliability of the retrieval. Applied to different weather conditions in Europe, the algorithm shows good performance both in the identification of precipitation areas and in the retrieval of precipitation, particularly valuable over the extremely variable environmental and meteorological conditions of the region. In particular, the PNPR is particularly efficient in: (1) screening and retrieval of precipitation over different background surfaces, (2) identification and retrieval of heavy rain for convective events, (3) identification of precipitation over cold/iced background, with some uncertainties affecting light precipitation. In this paper, examples of good agreement of precipitation pattern and intensity with ground-based data (radar and rain gauges) are provided for four different case studies. The algorithm has been developed in order to be easily tailored to new radiometers as they become available (such as the cross-track scanning Suomi NPP ATMS) and it is suitable for operational use as it is computationally very efficient. PNPR has been recently extended for applications to Africa and Southern Atlantic regions, and an extended validation over these regions (using two years of data acquired by the Tropical Rainfall Measuring Mission Precipitation Radar for comparison) is subject of a paper in preparation. The PNPR is currently used operationally within the EUMETSAT Hydrology Satellite Application Facility (H-SAF) to provide instantaneous precipitation from passive microwave cross-track scanning radiometers. It undergoes routinely through extensive validation over Europe carried out by the H-SAF Precipitation Products Validation Group.

scholarly journals The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies

2015 ◽  
Vol 8 (2) ◽  
pp. 837-857 ◽  
Author(s):  
P. Sanò ◽  
G. Panegrossi ◽  
D. Casella ◽  
F. Di Paola ◽  
L. Milani ◽  
...  
Keyword(s):  
Neural Network ◽  
Case Studies ◽  
Heavy Rain ◽  
Passive Microwave ◽  
Advanced Technology ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Precipitation Pattern ◽  
Neural Network Approach ◽  
Cross Track

Abstract. The purpose of this study is to describe a new algorithm based on a neural network approach (Passive microwave Neural network Precipitation Retrieval – PNPR) for precipitation rate estimation from AMSU/MHS observations, and to provide examples of its performance for specific case studies over the European/Mediterranean area. The algorithm optimally exploits the different characteristics of Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) channels, and their combinations, including the brightness temperature (TB) differences of the 183.31 channels, with the goal of having a single neural network for different types of background surfaces (vegetated land, snow-covered surface, coast and ocean). The training of the neural network is based on the use of a cloud-radiation database, built from cloud-resolving model simulations coupled to a radiative transfer model, representative of the European and Mediterranean Basin precipitation climatology. The algorithm provides also the phase of the precipitation and a pixel-based confidence index for the evaluation of the reliability of the retrieval. Applied to different weather conditions in Europe, the algorithm shows good performance both in the identification of precipitation areas and in the retrieval of precipitation, which is particularly valuable over the extremely variable environmental and meteorological conditions of the region. The PNPR is particularly efficient in (1) screening and retrieval of precipitation over different background surfaces; (2) identification and retrieval of heavy rain for convective events; and (3) identification of precipitation over a cold/iced background, with increased uncertainties affecting light precipitation. In this paper, examples of good agreement of precipitation pattern and intensity with ground-based data (radar and rain gauges) are provided for four different case studies. The algorithm has been developed in order to be easily tailored to new radiometers as they become available (such as the cross-track scanning Suomi National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS)), and it is suitable for operational use as it is computationally very efficient. PNPR has been recently extended for applications to the regions of Africa and the South Atlantic, and an extended validation over these regions (using 2 yr of data acquired by the Tropical Rainfall Measuring Mission precipitation radar for comparison) is the subject of a paper in preparation. The PNPR is currently used operationally within the EUMETSAT Hydrology Satellite Application Facility (H-SAF) to provide instantaneous precipitation from passive microwave cross-track scanning radiometers. It undergoes routinely thorough extensive validation over Europe carried out by the H-SAF Precipitation Products Validation Team.

scholarly journals Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS)

2020 ◽  
Vol 12 (19) ◽  
pp. 3160
Author(s):  
Yan Zhou ◽  
Christopher Grassotti
Keyword(s):  
Neural Network ◽  
Bias Correction ◽  
Retrieval System ◽  
Forward Model ◽  
Training Data ◽  
Advanced Technology ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Neural Network Approach ◽  
Dynamic Bias

We present the development of a dynamic over-ocean radiometric bias correction for the Microwave Integrated Retrieval System (MiRS) which accounts for spatial, temporal, spectral, and angular dependence of the systematic differences between observed and forward model-simulated radiances. The dynamic bias correction, which utilizes a deep neural network approach, is designed to incorporate dependence on the atmospheric and surface conditions that impact forward model biases. The approach utilizes collocations of observed Suomi National Polar-orbiting Partnership/Advanced Technology Microwave Sounder (SNPP/ATMS) radiances and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses which are used as input to the Community Radiative Transfer Model (CRTM) forward model to develop training data of radiometric biases. Analysis of the neural network performance indicates that in many channels, the dynamic bias is able to reproduce realistically both the spatial patterns of the original bias and its probability distribution function. Furthermore, retrieval impact experiments on independent data show that, compared with the baseline static bias correction, using the dynamic bias correction can improve temperature and water vapor profile retrievals, particularly in regions with higher Cloud Liquid Water (CLW) amounts. Ocean surface emissivity retrievals are also improved, for example at 23.8 GHz, showing an increase in correlation from 0.59 to 0.67 and a reduction of standard deviation from 0.035 to 0.026.

scholarly journals HAMP – the microwave package on the High Altitude and LOng range research aircraft HALO

2014 ◽  
Vol 7 (5) ◽  
pp. 4623-4657
Author(s):  
M. Mech ◽  
E. Orlandi ◽  
S. Crewell ◽  
F. Ament ◽  
L. Hirsch ◽  
...  
Keyword(s):  
High Altitude ◽  
Long Range ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Lower Atmosphere ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Calculations ◽  
Cloud Radar ◽  
Research Aircraft

Abstract. An advanced package of microwave remote sensing instrumentation has been developed for the operation on the new German High Altitude LOng range research aircraft (HALO). The HALO Microwave Package, HAMP, consists of two nadir looking instruments: a cloud radar at 36 GHz and a suite of passive microwave radiometers with 26 frequencies in different bands between 22.24 and 183.31 ± 12.5 GHz. We present a description of HAMP's instrumentation together with an illustration of its potential. To demonstrate this potential synthetic measurements for the implemented passive microwave frequencies and the cloud radar based on cloud resolving and radiative transfer model calculations were performed. These illustrate the advantage of HAMP's chosen frequency coverage, which allows for improved detection of hydrometeors both via the emission and scattering of radiation. Regression algorithms compare HAMP retrieval with standard satellite instruments from polar orbiters and show its advantages particularly for the lower atmosphere with a reduced root mean square error by 5 and 15% for temperature and humidity, respectively. HAMP's main advantage is the high spatial resolution of about 1 km which is illustrated by first measurements from test flights. Together these qualities make it an exciting tool for gaining better understanding of cloud processes, testing retrieval algorithms, defining future satellite instrument specifications, and validating platforms after they have been placed in orbit.

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.

scholarly journals Local Tilt Optimization of Photovoltaic Solar Panels for Maximum Radiation Absorption

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Mauro Masili ◽  
Liliane Ventura
Keyword(s):  
Solar Radiation ◽  
Tilt Angle ◽  
Spectral Response ◽  
Solar Spectrum ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Radiation Absorption ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Solar Panels

Incident solar radiation on photovoltaic (PV) solar panels is not constant throughout the year. Besides dependence on the season, solar radiation is reliant on the location and weather conditions. For a given location on Earth, the best-fixed orientation of a PV panel can be determined by achieving the maximum incident solar irradiance throughout the year or for a predetermined period. In this paper, we use a sophisticated atmospheric radiative transfer model to calculate the direct and diffuse solar irradiation (radiant exposure) for the solar spectrum incident on PV solar panels to determine the best tilt angle of the panel in order to maximize absorption of solar radiation for selected periods. We used the Regula-Falsi numerical method to obtain the tilt angle at which the derivative of solar irradiation (concerning the tilt angle) approaches zero. Moreover, the spectral response of typical silicon cells is taken into account. These calculations were carried out in São Carlos (SP), a town in the southeast of Brazil. The best tilt angle was obtained for three selected periods. Additionally, we provide results for Southern latitudes ranging from 0° to −55° in steps of −5° for the meteorological seasons. We have shown that for each period, there is an increase in solar radiation absorption compared to the traditional installation angle based exclusively on the local latitude. These calculations can be extended to any location.

scholarly journals The Sensitivity of Rainfall Estimation to Error Assumptions in a Bayesian Passive Microwave Retrieval Algorithm

2015 ◽  
Vol 54 (2) ◽  
pp. 408-422 ◽  
Author(s):  
Gregory S. Elsaesser ◽  
Christian D. Kummerow
Keyword(s):  
General Circulation ◽  
Circulation Model ◽  
Mathematical Expression ◽  
Passive Microwave ◽  
Bayesian Probability ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Model Errors ◽  
Frequency Distributions

AbstractThe Goddard profiling algorithm (GPROF) uses Bayesian probability theory to retrieve rainfall over the global oceans. A critical component of GPROF and most Bayes theorem–based retrieval frameworks is the specification of uncertainty in the observations being utilized to retrieve the parameter of interest. In the case of GPROF, for any sensor, uncertainties in microwave brightness temperatures (Tbs) arise from radiative transfer model errors, satellite sensor noise and/or degradation, and nonlinear, scene-dependent Tb offsets added during sensor intercalibration procedures. All mentioned sources impact sensors in a varying fashion, in part because of sensor-dependent fields of view. It is found that small errors in assumed Tb uncertainty (ranging from 0.57 K at 10 GHz to 2.29 K at 85 GHz) lead to a 3.6% change in the retrieved global-average oceanic rainfall rate, and 10%–20% (20%–40%) shifts in the pixel-level (monthly) frequency distributions for given rainfall bins. A mathematical expression describing the sensitivity of retrieved rainfall to uncertainty is developed here. The strong global sensitivity is linked to rainfall variance scaling systematically as Tb varies. For ocean scenes, the same emission-dominated rainfall–Tb physics used in passive microwave rainfall retrieval is also responsible for the substantial underestimation (overestimation) of global rainfall if uncertainty is overestimated (underestimated). Proper uncertainties are required to quantify variability in surface rainfall, assess long-term trends, and provide robust rainfall benchmarks for general circulation model evaluations. The implications for assessing global and regional biases in active versus passive microwave rainfall products, and for achieving rainfall product agreement among a constellation of orbiting microwave radiometers [employed in the Global Precipitation Measurement (GPM) mission], are also discussed.

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