Characterizing the FY-3A Microwave Temperature Sounder Using the ECMWF Model

2011 ◽  
Vol 28 (11) ◽  
pp. 1373-1389 ◽  
Author(s):  
Qifeng Lu ◽  
William Bell ◽  
Peter Bauer ◽  
Niels Bormann ◽  
Carole Peubey
Keyword(s):  
Weather Prediction ◽  
Quadratic Function ◽  
Climate Science ◽  
Brightness Temperatures ◽  
Microwave Sounding Unit ◽  
Satellite Sensors ◽  
Microwave Sounding ◽  
Data Source ◽  
Nwp Model ◽  
Ecmwf Model

Abstract China’s Feng-Yun-3A (FY-3A), launched in May 2008, is the first in a series of seven polar-orbiting meteorological satellites planned for the next decade by China. The FY-3 series is set to become an important data source for numerical weather prediction (NWP), reanalysis, and climate science. FY-3A is equipped with a microwave temperature sounding instrument (MWTS). This study reports an assessment of the MWTS instrument using the ECMWF NWP model, radiative transfer modeling, and comparisons with equivalent observations from the Advanced Microwave Sounding Unit-A (AMSU-A). The study suggests the MWTS instrument is affected by biases related to large shifts, or errors, in the frequency of the channel passbands as well as radiometer nonlinearity. The passband shifts, relative to prelaunch measurements, are 55, 39, and 33 MHz for channels 2–4, respectively. Relative to the design specification the shifts are 60, 80, and 83 MHz, with uncertainties of ±2.5 MHz. The radiometer nonlinearity results in a positive bias in measured brightness temperatures and is manifested as a quadratic function of measured scene temperatures. By correcting for both of these effects the quality of the MWTS data is improved significantly, with the standard deviations of the (observed minus simulated) differences based on short-range forecast fields reduced by 30%–50% relative to simulations using prelaunch measurements of the passband, to values close to those observed for AMSU-A-equivalent channels. The new methodology could be applied to other microwave temperature sounding instruments and illustrates the value of NWP fields for the on-orbit characterization of satellite sensors.

scholarly journals Detection and thermal description of medicanes from numerical simulation

2014 ◽  
Vol 14 (5) ◽  
pp. 1059-1070 ◽  
Author(s):  
M. A. Picornell ◽  
J. Campins ◽  
A. Jansà
Keyword(s):  
Numerical Simulation ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Small Scale ◽  
Medium Range Weather Forecast ◽  
Warm Core ◽  
Strong Winds ◽  
Nwp Model ◽  
Ecmwf Model

Abstract. Tropical-like cyclones rarely affect the Mediterranean region but they can produce strong winds and heavy precipitations. These warm-core cyclones, called MEDICANES (MEDIterranean hurriCANES), are small in size, develop over the sea and are infrequent. For these reasons, the detection and forecast of medicanes are a difficult task and many efforts have been devoted to identify them. The goals of this work are to contribute to a proper description of these structures and to develop some criteria to identify medicanes from numerical weather prediction (NWP) model outputs. To do that, existing methodologies for detecting, characterizating and tracking cyclones have been adapted to small-scale intense cyclonic perturbations. First, a mesocyclone detection and tracking algorithm has been modified to select intense cyclones. Next, the parameters that define the Hart's cyclone phase diagram are tuned and calculated to examine their thermal structure. Four well-known medicane events have been described from numerical simulation outputs of the European Centre for Medium-Range Weather Forecast (ECMWF) model. The predicted cyclones and their evolution have been validated against available observational data and numerical analyses from the literature.

scholarly journals Connecting the Time Series of Microwave Sounding Observations from AMSU to ATMS for Long-Term Monitoring of Climate

2014 ◽  
Vol 31 (10) ◽  
pp. 2206-2222 ◽  
Author(s):  
Xiaolei Zou ◽  
Fuzhong Weng ◽  
H. Yang
Keyword(s):  
Time Series ◽  
Atmospheric Temperature ◽  
Advanced Technology ◽  
Central Frequency ◽  
Brightness Temperatures ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Cross Calibration ◽  
Term Monitoring

Abstract The measurements from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) on board NOAA polar-orbiting satellites have been extensively utilized for detecting atmospheric temperature trend during the last several decades. After the launch of the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite on 28 October 2011, MSU and AMSU-A time series will be overlapping with the Advanced Technology Microwave Sounder (ATMS) measurements. While ATMS inherited the central frequency and bandpass from most of AMSU-A sounding channels, its spatial resolution and noise features are, however, distinctly different from those of AMSU. In this study, the Backus–Gilbert method is used to optimally resample the ATMS data to AMSU-A fields of view (FOVs). The differences between the original and resampled ATMS data are demonstrated. By using the simultaneous nadir overpass (SNO) method, ATMS-resampled observations are collocated in space and time with AMSU-A data. The intersensor biases are then derived for each pair of ATMS–AMSU-A channels. It is shown that the brightness temperatures from ATMS now fall well within the AMSU data family after resampling and SNO cross calibration. Thus, the MSU–AMSU time series can be extended into future decades for more climate applications.

scholarly journals Reducing Uncertainties of SNO-Estimated Intersatellite AMSU-A Brightness Temperature Biases for Surface-Sensitive Channels

2008 ◽  
Vol 25 (6) ◽  
pp. 1048-1054 ◽  
Author(s):  
Robert A. Iacovazzi ◽  
Changyong Cao
Keyword(s):  
Brightness Temperature ◽  
Nearest Neighbor ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Surface Radiation ◽  
Bias Estimation ◽  
Microwave Sounding Unit ◽  
Collocation Technique ◽  
Microwave Sounding ◽  
Microwave Radiometers

Abstract In this study, a technique has been developed to improve collocation of two passive-microwave satellite instrument datasets at a simultaneous nadir overpass (SNO). The technique has been designed for the purpose of reducing uncertainties related to SNO-inferred intersatellite brightness temperature (Tb) biases, and it involves replacing the current “nearest-neighbor pixel matching” collocation technique with quality-controlled bilinear interpolation. Since the largest Tb bias estimation uncertainties of the SNO method are associated with highly variable earth scenes and window channels of microwave radiometers that have relatively large (∼50 km) separation between measurements, the authors have used Advanced Microwave Sounding Unit A (AMSU-A) data to develop the technique. It is found that using the new data collocation technique reduces SNO ensemble mean Tb bias confidence intervals in the SNO method, as applied to surface-sensitive channels of AMSU-A, by nearly 70% on average. This improvement in the SNO method enhances its ability to quantify intersatellite Tb biases at microwave radiometer channels that are sensitive to surface radiation, which is necessary to advance the sciences of numerical weather prediction and climate change detection.

scholarly journals Using MetOp-A AVHRR Clear-Sky Measurements to Cloud-Clear MetOp-A IASI Column Radiances

2011 ◽  
Vol 28 (9) ◽  
pp. 1104-1116 ◽  
Author(s):  
Eric S. Maddy ◽  
Thomas S. King ◽  
Haibing Sun ◽  
Walter W. Wolf ◽  
Christopher D. Barnet ◽  
...  
Keyword(s):  
High Spatial Resolution ◽  
Zero Bias ◽  
Clear Sky ◽  
Microwave Sounding Unit ◽  
Atmospheric Sounding ◽  
Microwave Sounding ◽  
Ecmwf Model ◽  
Skin Temperatures ◽  
Very High ◽  
Better Than

Abstract High spatial resolution measurements from the Advanced Very High Resolution Radiometer (AVHRR) on the Meteorological Operation (MetOp)-A satellite that are collocated to the footprints from the Infrared Atmospheric Sounding Interferometer (IASI) on the satellite are exploited to improve and quality control cloud-cleared radiances obtained from the IASI. For a partial set of mostly ocean MetOp-A orbits collected on 3 October 2010 for latitudes between 70°S and 75°N, these cloud-cleared radiances and clear-sky subpixel AVHRR measurements within the IASI footprint agree to better than 0.25-K root-mean-squared difference for AVHRR window channels with almost zero bias. For the same dataset, surface skin temperatures retrieved using the combined AVHRR, IASI, and Advanced Microwave Sounding Unit (AMSU) cloud-clearing algorithm match well with ECMWF model surface skin temperatures over ocean, yielding total uncertainties ≤1.2 K for scenes with up to 97% cloudiness.

scholarly journals Inter-Calibration of AMSU-A Window Channels

2020 ◽  
Vol 12 (18) ◽  
pp. 2988
Author(s):  
Wenze Yang ◽  
Huan Meng ◽  
Ralph R. Ferraro ◽  
Yong Chen
Keyword(s):  
Atmospheric Temperature ◽  
Global Information ◽  
Temperature Profiles ◽  
Climate Data ◽  
Error Sources ◽  
Bias Drift ◽  
Brightness Temperatures ◽  
Microwave Sounding Unit ◽  
Vapor Cloud ◽  
Microwave Sounding

More than one decade of observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard the polar-orbiting satellites NOAA-15 to NOAA-19 and European Meteorological Operational satellite program-A (MetOp-A) provided global information on atmospheric temperature profiles, water vapor, cloud, precipitation, etc. These observations were primarily intended for weather related prediction and applications, however, in order to meet the requirements for climate application, further reprocessing must be conducted to first eliminate any potential satellites biases. After the geolocation and cross-scan bias corrections were applied to the dataset, follow-on research focused on the comparison amongst AMSU-A window channels (e.g., 23.8, 31.4, 50.3 and 89.0 GHz) from the six different satellites to remove any inter-satellite inconsistency. Inter-satellite differences can arise from many error sources, such as bias drift, sun-heating-induced instrument variability in brightness temperatures, radiance dependent biases due to inaccurate calibration nonlinearity, etc. The Integrated microwave inter-calibration approach (IMICA) approach was adopted in this study for inter-satellite calibration of AMSU-A window channels after the appropriate standard deviation (STD) thresholds were identified to restrict Simultaneous Nadir Overpass (SNO) data for window channels. This was a critical step towards the development of a set of fundamental and thematic climate data records (CDRs) for hydrological and climatological applications. NOAA-15 served as the main reference satellite for this study. For ensuing studies that expand to beyond 2015, however, it is recommended that a different satellite be adopted as the reference due to concerns over potential degradation of NOAA-15 AMSU-A.

Characterizing Channel Center Frequencies in AMSU-A and MSU Microwave Sounding Instruments

2014 ◽  
Vol 31 (8) ◽  
pp. 1713-1732 ◽  
Author(s):  
Qifeng Lu ◽  
William Bell
Keyword(s):  
Radiative Transfer ◽  
Weather Prediction ◽  
Pass Band ◽  
Frequency Locking ◽  
Infrared Observation ◽  
Band Center ◽  
Dominant Component ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Observation Bias

Abstract Passive microwave observations from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) have been exploited widely for numerical weather prediction (NWP), atmospheric reanalyses, and climate monitoring studies. The treatment of biases in these observations, with respect to models as well as between satellites, has been the focus of much effort in recent years. This study presents evidence that shifts, drifts, and uncertainties in pass band center frequencies are a significant contribution to these biases. Center frequencies for AMSU-A channels 6–14 and MSU channel 3 have been analyzed using NWP fields and radiative transfer models, for a series of operational satellites covering the period 1979–2012. AMSU-A channels 6 (54.40 GHz), 7 (54.94 GHz), and 8 (55.50 GHz) on several satellites exhibit significant shifts and drifts relative to nominal pass band center frequencies. No significant shifts were found for AMSU-A channels 9–14, most probably as a consequence of the active frequency locking of these channels. For MSU channel 3 (54.96 GHz) most satellites exhibit large shifts, the largest for the earliest satellites. For example, for the first MSU on the Television and Infrared Observation Satellite-N (TIROS-N), the analyzed shift is 68 MHz over the lifetime of the satellite. Taking these shifts into account in the radiative transfer modeling significantly improves the fit between model and observations, eliminates the strong seasonal cycle in the model–observation misfit, and significantly improves the bias between NWP models and observations. The study suggests that, for several channels studied, the dominant component of the model–observation bias results from these spectral errors, rather than radiometric bias due to calibration errors.

scholarly journals Detection and thermal description of medicanes from numerical simulation

2013 ◽  
Vol 1 (6) ◽  
pp. 7417-7447 ◽  
Author(s):  
M. A. Picornell ◽  
J. Campins ◽  
A. Jansà
Keyword(s):  
Numerical Simulation ◽  
Mediterranean Region ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Small Scale ◽  
Warm Core ◽  
Detection And Tracking ◽  
Strong Winds ◽  
Nwp Model ◽  
Ecmwf Model

Abstract. Tropical-like cyclones rarely affect the Mediterranean region and they can produce strong winds and heavy precipitations. These warm-core cyclones, called MEDICANES (MEDIterranean hurriCANES), are small size, develop over the sea and are infrequent. For these reasons, the detection and forecast of medicanes are a difficult task and many efforts have been devoted to identify them. The goals of this work are to contribute to a proper description of these structures and to develop some criteria to identify medicanes from numerical weather prediction (NWP) model outputs. To do that, existing methodologies for detecting, characterizating and tracking cyclones have been adapted to small-scale intense cyclonic perturbations. First, a mesocyclone detection and tracking algorithm has been modified to select intense cyclones. Next, the parameters that define the Hart's cyclone phase diagram are tuned and calculated to examine their thermal structure. Four well-known medicane events have been described from numerical simulation outputs of the ECMWF model. The predicted cyclones and their evolution have been validated against available observational data and numerical analyses from literature.

scholarly journals On the Accuracy of Vaisala RS41 versus RS92 Upper-Air Temperature Observations

2019 ◽  
Vol 36 (4) ◽  
pp. 635-653 ◽  
Author(s):  
Bomin Sun ◽  
Tony Reale ◽  
Steven Schroeder ◽  
Michael Pettey ◽  
Ryan Smith
Keyword(s):  
Radio Occultation ◽  
Reference Data ◽  
Weather Prediction ◽  
Elevation Angle ◽  
Data Sources ◽  
Cold Bias ◽  
Stratospheric Temperature ◽  
Nwp Model ◽  
The University ◽  
Ecmwf Model

AbstractThe accuracy of Vaisala RS92 versus RS41 global radiosonde soundings, emphasizing stratospheric temperature, is assessed from January 2015 to June 2017 using ~311 500 RS92 and ~65 800 RS41 profiles and three different reference data sources. First, numerical weather prediction (NWP) model outputs are used as a transfer medium to produce relative RS92 and RS41 comparisons by analyzing observation minus NWP model background (OB–BG) and observation minus analysis (OB–AN) differences using the NOAA Climate Forecast System Reanalysis (CFSR; both comparisons) and the operational European Centre for Medium-Range Weather Forecasts (ECMWF) model (OB–AN comparison only). Second, GPS radio occultation (GPSRO) dry temperature profiles are directly compared with radiosondes, using GPSRO data from the University Corporation for Atmospheric Research (UCAR) Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and EUMETSAT Radio Occultation Meteorology (ROM) Satellite Application Facility (SAF). Third, dual launches (RS92 and RS41 suspended from the same balloon) at five sites allow direct assessments. Comparisons of RS92 versus RS41 from all reference data sources are basically consistent. These two sondes agree well with global average temperature differences <0.1–0.2 K in the lower stratosphere from 51.5 to 26.1 hPa based on global stations and the dual launches. RS41 appears to be less sensitive than RS92 to changes in solar elevation angle. This study indicates that nighttime RS92 and RS41 radiosonde temperature biases are negligible, but infers a stratospheric cold bias (<0.5 K) in the CFSR and ECMWF model data.

scholarly journals Retrieval of an ice water path over the ocean from ISMAR and MARSS millimeter and submillimeter brightness temperatures

2018 ◽  
Vol 11 (1) ◽  
pp. 611-632 ◽  
Author(s):  
Manfred Brath ◽  
Stuart Fox ◽  
Patrick Eriksson ◽  
R. Chawn Harlow ◽  
Martin Burgdorf ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Weather Prediction ◽  
Liquid Water Path ◽  
Retrieval Method ◽  
Water Path ◽  
Brightness Temperatures ◽  
Ice Water Path ◽  
The Neural Networks ◽  
Nwp Model ◽  
Ice Water

Abstract. A neural-network-based retrieval method to determine the snow ice water path (SIWP), liquid water path (LWP), and integrated water vapor (IWV) from millimeter and submillimeter brightness temperatures, measured by using airborne radiometers (ISMAR and MARSS), is presented. The neural networks were trained by using atmospheric profiles from the ICON numerical weather prediction (NWP) model and by radiative transfer simulations using the Atmospheric Radiative Transfer Simulator (ARTS). The basic performance of the retrieval method was analyzed in terms of offset (bias) and the median fractional error (MFE), and the benefit of using submillimeter channels was studied in comparison to pure microwave retrievals. The retrieval is offset-free for SIWP  > 0.01 kg m−2, LWP  > 0.1 kg m−2, and IWV  > 3 kg m−2. The MFE of SIWP decreases from 100 % at SIWP  =  0.01 kg m−2 to 20 % at SIWP  =  1 kg m−2 and the MFE of LWP from 100 % at LWP  = 0.05 kg m−2 to 30 % at LWP  =  1 kg m−2. The MFE of IWV for IWV  > 3 kg m−2 is 5 to 8 %. The SIWP retrieval strongly benefits from submillimeter channels, which reduce the MFE by a factor of 2, compared to pure microwave retrievals. The IWV and the LWP retrievals also benefit from submillimeter channels, albeit to a lesser degree. The retrieval was applied to ISMAR and MARSS brightness temperatures from FAAM flight B897 on 18 March 2015 of a precipitating frontal system west of the coast of Iceland. Considering the given uncertainties, the retrieval is in reasonable agreement with the SIWP, LWP, and IWV values simulated by the ICON NWP model for that flight. A comparison of the retrieved IWV with IWV from 12 dropsonde measurements shows an offset of 0.5 kg m−2 and an RMS difference of 0.8 kg m−2, showing that the retrieval of IWV is highly effective even under cloudy conditions.

scholarly journals Global 4DVAR Assimilation and Forecast Experiments Using AMSU Observations over Land. Part I: Impacts of Various Land Surface Emissivity Parameterizations

2010 ◽  
Vol 25 (1) ◽  
pp. 5-19 ◽  
Author(s):  
Fatima Karbou ◽  
Elisabeth Gérard ◽  
Florence Rabier
Keyword(s):  
Cost Function ◽  
Land Surface ◽  
Feasibility Studies ◽  
Brightness Temperatures ◽  
Function Change ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
The Cost ◽  
Assimilation System ◽  
Made In

Abstract To improve the assimilation of Advanced Microwave Sounding Unit-A and -B (AMSU-A and -B) observations over land, three methods, based either on an estimation of the land emissivity or the land skin temperature directly from satellite observations, have been developed. Some feasibility studies have been performed in the Météo-France assimilation system in order to choose the most appropriate method for the system. This study reports on three 2-month assimilation and forecast experiments that use different methods to estimate AMSU-A and -B land emissivities together with the operational run as a control experiment. The experiments and the control have been subjected to several comparisons. The performance of the observation operator for simulating window channel brightness temperatures has been studied. The study shows considerable improvements in the statistics of the window channels’ first-guess departures (bias, standard deviation). The correlations between the observations and the model’s simulations have also been improved, especially over snow-covered areas. The performances of the assimilation system, in terms of cost function change, have been examined: the cost function is generally improved during the screening and remains stable during the minimization. Moreover, comparisons have been made in terms of impacts on both analyses and forecasts.

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