Efficient Model-Based Estimation of Atmospheric Transmittance and Ocean Wind Direction from WindSat Polarimetric Microwave Radiometer Data

2006 ◽  
Author(s):  
D.-J. Kim ◽  
D. Lyzenga
Keyword(s):  
Wind Direction ◽  
Microwave Radiometer ◽  
Model Based ◽  
Polarimetric Microwave Radiometer ◽  
Atmospheric Transmittance ◽  
Ocean Wind

scholarly journals A model-based approach to adjust microwave observations for operational applications: results of a campaign at Munich Airport in winter 2011/2012

2013 ◽  
Vol 6 (10) ◽  
pp. 2879-2891 ◽  
Author(s):  
J. Güldner
Keyword(s):  
Neural Network ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Regression Method ◽  
Training Data ◽  
Data Set ◽  
Numerical Weather ◽  
Model Based ◽  
Temperature And Humidity ◽  
Airport Site

Abstract. In the frame of the project "LuFo iPort VIS" which focuses on the implementation of a site-specific visibility forecast, a field campaign was organised to offer detailed information to a numerical fog model. As part of additional observing activities, a 22-channel microwave radiometer profiler (MWRP) was operating at the Munich Airport site in Germany from October 2011 to February 2012 in order to provide vertical temperature and humidity profiles as well as cloud liquid water information. Independently from the model-related aims of the campaign, the MWRP observations were used to study their capabilities to work in operational meteorological networks. Over the past decade a growing quantity of MWRP has been introduced and a user community (MWRnet) was established to encourage activities directed at the set up of an operational network. On that account, the comparability of observations from different network sites plays a fundamental role for any applications in climatology and numerical weather forecast. In practice, however, systematic temperature and humidity differences (bias) between MWRP retrievals and co-located radiosonde profiles were observed and reported by several authors. This bias can be caused by instrumental offsets and by the absorption model used in the retrieval algorithms as well as by applying a non-representative training data set. At the Lindenberg observatory, besides a neural network provided by the manufacturer, a measurement-based regression method was developed to reduce the bias. These regression operators are calculated on the basis of coincident radiosonde observations and MWRP brightness temperature (TB) measurements. However, MWRP applications in a network require comparable results at just any site, even if no radiosondes are available. The motivation of this work is directed to a verification of the suitability of the operational local forecast model COSMO-EU of the Deutscher Wetterdienst (DWD) for the calculation of model-based regression operators in order to provide unbiased vertical profiles during the campaign at Munich Airport. The results of this algorithm and the retrievals of a neural network, specially developed for the site, are compared with radiosondes from Oberschleißheim located about 10 km apart from the MWRP site. Outstanding deviations for the lowest levels between 50 and 100 m are discussed. Analogously to the airport experiment, a model-based regression operator was calculated for Lindenberg and compared with both radiosondes and operational results of observation-based methods. The bias of the retrievals could be considerably reduced and the accuracy, which has been assessed for the airport site, is quite similar to those of the operational radiometer site at Lindenberg above 1 km height. Additional investigations are made to determine the length of the training period necessary for generating best estimates. Thereby three months have proven to be adequate. The results of the study show that on the basis of numerical weather prediction (NWP) model data, available everywhere at any time, the model-based regression method is capable of providing comparable results at a multitude of sites. Furthermore, the approach offers auspicious conditions for automation and continuous updating.

Cross-Polarized Synthetic Aperture Radar: A New Potential Measurement Technique for Hurricanes

2012 ◽  
Vol 93 (4) ◽  
pp. 531-541 ◽  
Author(s):  
Biao Zhang ◽  
William Perrie
Keyword(s):  
Wind Speed ◽  
Synthetic Aperture Radar ◽  
Wind Direction ◽  
Incidence Angle ◽  
Microwave Radiometer ◽  
Synthetic Aperture ◽  
Cross Polarization ◽  
Potential Measurement ◽  
Wind Retrievals ◽  
Aperture Radar

We present an empirical C-band Cross-Polarization Ocean (C-2PO) model for wind retrievals from synthetic aperture radar (SAR) data collected by the RADARSAT-2 satellite. The C-2PO model relates normalized radar cross section (NRCS) in cross polarization to wind speed at 10-m height. This wind retrieval model has the characteristic that it is independent of wind direction and radar incidence angle but is quite linear with respect to wind speed. To evaluate the accuracy of the proposed model, winds with a resolution on the scale of 1 km were retrieved from a dual-polarization SAR image of Hurricane Earl on 2 September 2010, using the C-2PO model and compared with CMOD5.N, the newest available C-band geophysical model function (GMF), and validated with collocated airborne stepped-frequency microwave radiometer measurements and National Data Buoy Center data. Results suggest that for winds up to 38 m s−1, C-2PO has a bias of −0.89 m s−1 and a root-meansquare error of 3.23 m s−1 compared to CMOD5.N, which has a bias of −4.14 m s−1 and an rms difference of 6.24 m s−1. Similar results are obtained from Hurricane Ike, comparing wind retrievals from C-2PO and CMOD5.N with H*Wind data. The advantage of C-2PO over CMOD5.N and other GMFs is that it does not need any external wind direction and radar incidence angle inputs. Moreover, in the presently available quad-polarization dataset, C-2PO has the feature that the cross-polarized NRCS linearly increases even for wind speeds up to 26 m s−1 and reproduces the hurricane eye structure well, thereby providing a potential technique for hurricane observations from space.

A New Hurricane Wind Direction Retrieval Method for SAR Images without Hurricane Eye

2018 ◽  
Vol 35 (11) ◽  
pp. 2229-2239 ◽  
Author(s):  
Yuan Gao ◽  
Changlong Guan ◽  
Jian Sun ◽  
Lian Xie
Keyword(s):  
Wind Direction ◽  
Microwave Radiometer ◽  
Estimation Method ◽  
Band Model ◽  
Sar Images ◽  
Wind Speeds ◽  
Research Division ◽  
External Data ◽  
Hurricane Wind ◽  
Good Potential

AbstractThis study presents a new approach for hurricane wind direction retrieval utilizing rainband streaks contained in synthetic aperture radar (SAR) images without hurricane eye information, based on the hurricane inflow angle. To calculate the wind direction field, a method for estimating the location of the hurricane center is given. In this paper, four Sentinel-1A (S-1A) images with a hurricane eye are used to clarify the center estimation method. Three S-1A SAR images without a hurricane eye are studied to evaluate the accuracy of the new method. The estimated locations of hurricane centers show good agreement with hurricane track data provided by the National Oceanic and Atmospheric Administration (NOAA)’s Atlantic Oceanographic and Meteorological Laboratory (AOML) Hurricane Research Division (HRD), HurricaneCity, and the National Institute of Informatics (NII). To validate the estimated wind directions, the NOAA HRD dropwindsonde observations for Tropical Storm Karl are collected and compared. The wind directions retrieved by our approach are more consistent with visual inspection than the fast Fourier transform (FFT) method in subimages. Moreover, the retrieved wind speeds utilizing C-band model 5.N (CMOD5.N) are compared with wind speed estimations observed by Stepped Frequency Microwave Radiometer (SFMR). The results suggest that the proposed method has good potential to retrieve hurricane wind direction from SAR images without a hurricane eye and external data.

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