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.

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 Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 74
Author(s):  
Yajie Qi ◽  
Shuiyong Fan ◽  
Bai Li ◽  
Jiajia Mao ◽  
Dawei Lin
Keyword(s):  
Control Experiment ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Radar Data ◽  
Specific Humidity ◽  
Rainfall Forecast ◽  
Hourly Rainfall ◽  
Microwave Radiometers ◽  
The Impact ◽  
Precipitation Enhancement

Ground-based microwave radiometers (MWRPS) can provide continuous atmospheric temperature and relative humidity profiles for a weather prediction model. We investigated the impact of assimilation of ground-based microwave radiometers based on the rapid-refresh multiscale analysis and prediction system-short term (RMAPS-ST). In this study, five MWRP-retrieved profiles were assimilated for the precipitation enhancement that occurred in Beijing on 21 May 2020. To evaluate the influence of their assimilation, two experiments with and without the MWRPS assimilation were set. Compared to the control experiment, which only assimilated conventional observations and radar data, the MWRPS experiment, which assimilated conventional observations, the ground-based microwave radiometer profiles and the radar data, had a positive impact on the forecasts of the RMAPS-ST. The results show that in comparison with the control test, the MWRPS experiment reproduced the heat island phenomenon in the observation better. The MWRPS assimilation reduced the bias and RMSE of two-meter temperature and two-meter specific humidity forecasting in the 0–12 h of the forecast range. Furthermore, assimilating the MWRPS improved both the distribution and the intensity of the hourly rainfall forecast, as compared with that of the control experiment, with observations that predicted the process of the precipitation enhancement in the urban area of Beijing.

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.

Advanced Rain/No-Rain Classification Methods for Microwave Radiometer Observations over Land

2008 ◽  
Vol 47 (11) ◽  
pp. 3016-3029 ◽  
Author(s):  
Shinta Seto ◽  
Takuji Kubota ◽  
Nobuhiro Takahashi ◽  
Toshio Iguchi ◽  
Taikan Oki
Keyword(s):  
Brightness Temperature ◽  
Land Surface ◽  
Microwave Radiometer ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Retrieval Algorithm ◽  
Microwave Imager ◽  
Microwave Radiometers ◽  
Trmm Microwave Imager ◽  
Global Precipitation

Abstract Seto et al. developed rain/no-rain classification (RNC) methods over land for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). In this study, the methods are modified for application to other microwave radiometers. The previous methods match TMI observations with TRMM precipitation radar (PR) observations, classify the TMI pixels into rain pixels and no-rain pixels, and then statistically summarize the observed brightness temperature at the no-rain pixels into a land surface brightness temperature database. In the modified methods, the probability distribution of brightness temperature under no-rain conditions is derived from unclassified TMI pixels without the use of PR. A test with the TMI shows that the modified (PR independent) methods are better than the RNC method developed for the Goddard profiling algorithm (GPROF; the standard algorithm for the TMI) while they are slightly poorer than corresponding previous (PR dependent) methods. M2d, one of the PR-independent methods, is applied to observations from the Advanced Microwave Scanning Radiometer for Earth Observing Satellite (AMSR-E), is evaluated for a matchup case with PR, and is evaluated for 1 yr with a rain gauge dataset in Japan. M2d is incorporated into a retrieval algorithm developed by the Global Satellite Mapping of Precipitation project to be applied for the AMSR-E. In latitudes above 30°N, the rain-rate retrieval is compared with a rain gauge dataset by the Global Precipitation Climatology Center. Without a snow mask, a large amount of false rainfall due to snow contamination occurs. Therefore, a simple snow mask using the 23.8-GHz channel is applied and the threshold of the mask is optimized. Between 30° and 60°N, the optimized snow mask forces the miss of an estimated 10% of the total rainfall.

scholarly journals NOAA Operational Microwave Sounding Radiometer Data Quality Monitoring and Anomaly Assessment Using COSMIC GNSS Radio-Occultation Soundings

2020 ◽  
Vol 12 (5) ◽  
pp. 828
Author(s):  
Robbie Iacovazzi ◽  
Lin Lin ◽  
Ninghai Sun ◽  
Quanhua Liu
Keyword(s):  
Data Quality ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Advanced Technology ◽  
Quality Monitoring ◽  
Radiative Transfer Model ◽  
Double Difference ◽  
Transfer Model ◽  
Microwave Sounding

National Oceanic and Atmospheric Administration (NOAA) operational Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit-A (AMSU-A) data used in numerical weather prediction and climate analysis are essential to protect life and property and maintain safe and efficient commerce. Routine data quality monitoring and anomaly assessment is important to sustain data effectiveness. One valuable parameter used to monitor microwave sounder data quality is the antenna temperature (Ta) difference (O-B) computed between direct instrument Ta measurements and forward radiative transfer model (RTM) brightness temperature (Tb) simulations. This requires microwave radiometer data to be collocated with atmospheric temperature and moisture sounding profiles, so that representative boundary conditions are used to produce the RTM-simulated Tb values. In this study, Constellation Observing System for Meteorology, Ionosphere, and Climate/Formosa Satellite Mission 3 (COSMIC) Global Navigation Satellite System (GNSS) Radio Occultation (RO) soundings over the ocean and equatorward of 60° latitude are used as input to the Community RTM (CRTM) to generate simulated NOAA-18, NOAA-19, Metop-A, and Metop-B AMSU-A and S-NPP and NOAA-20 ATMS Tb values. These simulated Tb values, together with observed Ta values that are nearly simultaneous in space and time, are used to compute Ta O-B statistics on monthly time scales for each instrument. In addition, the CRTM-simulated Tb values based on the COSMIC GNSS RO soundings can be used as a transfer standard to inter-compare Ta values from different microwave radiometer makes and models that have the same bands. For example, monthly Ta O-B statistics for NOAA-18 AMSU-A Channels 4–12 and NOAA-20 ATMS Channels 5–13 can be differenced to estimate the “double-difference” Ta biases between these two instruments for the corresponding frequency bands. This study reveals that the GNSS RO soundings are critical to monitoring and trending individual instrument O-B Ta biases and inter-instrument “double-difference” Ta biases and also to estimate impacts of some sensor anomalies on instrument Ta values.

scholarly journals A New Method for Hail Detection from the GPM Constellation: A Prospect for a Global Hailstorm Climatology

2020 ◽  
Vol 12 (21) ◽  
pp. 3553
Author(s):  
Sante Laviola ◽  
Giulio Monte ◽  
Vincenzo Levizzani ◽  
Ralph R. Ferraro ◽  
James Beauchamp
Keyword(s):  
Global Scale ◽  
New Method ◽  
Frequency Range ◽  
Detection Model ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Temporal Rate ◽  
Microwave Radiometers ◽  
Global Precipitation ◽  
Very High

A new method for detecting hailstorms by using all the MHS-like (MHS, Microwave Humidity Sounder) satellite radiometers currently in orbit is presented. A probability-based model originally designed for AMSU-B/MHS-based (AMSU-B, Advanced Microwave Sounding Unit-B) radiometers has been fitted to the observations of all microwave radiometers onboard the satellites of the Global Precipitation Measurements (GPM) constellation. All MHS-like frequency channels in the 150–170 GHz frequency range were adjusted on the MHS channel 2 (157 GHz) in order to account for the instrumental differences and tune the original model on the MHS-like technical characteristics. The novelty of this approach offers the potential of retrieving a uniform and homogeneous hail dataset on the global scale. The application of the hail detection model to the entire GPM constellation demonstrates the high potential of this generalized model to map the evolution of hail-bearing systems at very high temporal rate. The results on the global scale also demonstrate the high performances of the hail model in detecting the differences of hailstorm structure across the two hemispheres by means of a thorough reconstruction of the seasonality of the events particularly in South America where the largest hailstones are typically observed.

scholarly journals Supercooled Liquid Water Clouds observed and analysed at the Top of the Planetary Boundary Layer above Dome C, Antarctica

2019 ◽  
Author(s):  
Philippe Ricaud ◽  
Massimo Del Guasta ◽  
Eric Bazile ◽  
Niramson Azouz ◽  
Angelo Lupi ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Liquid Water ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Mixing Layer ◽  
Austral Summer ◽  
Supercooled Liquid ◽  
Surface Radiation ◽  
Capping Inversion

Abstract. A comprehensive analysis of the water budget over the Dome C (Concordia, Antarctica) station has been performed during the austral summer 2018–2019 as part of the Year of Polar Prediction (YOPP) international campaign. Thin (~ 100-m) supercooled liquid water (SLW) clouds have been detected and analysed using remotely sensed observations at the station (tropospheric depolarization LIDAR, microwave radiometer HAMSTRAD, net surface radiation from Baseline Surface Radiation Network, BSRN), radiosondes and using satellite observations (CALIOP/CALIPSO) combined with a specific configuration of the Numerical Weather Prediction model: ARPEGE-SH. Two case studies are used to illustrate this phenomenon. On 24 December 2018, the atmospheric planetary boundary layer (PBL) evolved following a typical diurnal variation, that is to say with a warm and dry mixing layer at local noon thicker than the cold and dry stable layer at local midnight. Our study showed that the SLW clouds were observed at Dome C within the entrainment and the capping inversion zones at the top of the PBL. ARPEGE-SH was not able to correctly estimate the ratio between liquid and solid water inside the clouds. The SLW content was always strongly underestimated in the studied cases. The lack of simulated SLW in the model impacted the net surface radiation that was 20–30 W m−2 higher in the BSRN observations than in the ARPEGE-SH calculations, mainly attributable to longwave downward surface radiation from BSRN being 50 W m−2 greater than that of ARPEGE-SH. On 20 December 2018, a warm and wet episode impacted the PBL with no clear diurnal cycle of the PBL top height. SLW cloud appearance coincided with the warm and wet event within the entrainment and capping inversion zones. The amount of liquid water measured by HAMSTRAD was ~ 20 times greater in this perturbed PBL than in the typical PBL. Since ARPEGE-SH was not able to accurately reproduce these SLW clouds, the discrepancy between the observed and calculated net surface radiation was even greater than in the typical PBL period, reaching + 50 W m−2, mainly attributable to longwave downward surface radiation from BSRN being 100 W m−2 greater than that of ARPEGE-SH. The absence of SLW clouds in NWPs over Antarctica may indicate an incorrect simulation of the radiative budget of the polar atmosphere.

scholarly journals The Radiometric Sensitivity Requirements for Satellite Microwave Temperature Sounding Instruments for Numerical Weather Prediction

2010 ◽  
Vol 27 (3) ◽  
pp. 443-456 ◽  
Author(s):  
William Bell ◽  
Sabatino Di Michele ◽  
Peter Bauer ◽  
Tony McNally ◽  
Stephen J. English ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Forecast Errors ◽  
Atmospheric Infrared Sounder ◽  
Microwave Sounding Unit ◽  
Atmospheric Sounding ◽  
Microwave Sounding ◽  
Satellite Microwave ◽  
The Impact

Abstract The sensitivity of NWP forecast accuracy with respect to the radiometric performance of microwave sounders is assessed through a series of observing system experiments at the Met Office and ECMWF. The observing system experiments compare the impact of normal data from a single Advanced Microwave Sounding Unit (AMSU) with that from an AMSU where synthetic noise has been added. The results show a measurable reduction in forecast improvement in the Southern Hemisphere, with improvements reduced by 11% for relatively small increases in radiometric noise [noise-equivalent brightness temperature (NEΔT) increased from 0.1 to 0.2 K for remapped data]. The impact of microwave sounding data is shown to be significantly less than was the case prior to the use of advanced infrared sounder data [Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI)], with microwave sounding data now reducing Southern Hemisphere forecast errors by approximately 10% compared to 40% in the pre-AIRS/IASI period.

scholarly journals A Study of the NOAA Near-Nadir AMSU-A Brightness Temperatures over Antarctica

2010 ◽  
Vol 27 (6) ◽  
pp. 995-1004 ◽  
Author(s):  
Tsan Mo
Keyword(s):  
Time Series ◽  
Test Site ◽  
Good Test ◽  
Brightness Temperatures ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Diurnal Effect ◽  
Natural Test ◽  
Microwave Radiometers ◽  
The Antarctic

Abstract Daily mean brightness temperatures over Antarctica derived from measurements by three Advanced Microwave Sounding Unit-A (AMSU-A) radiometers on board NOAA-18, NOAA-19, and MetOp-A satellites are studied. To demonstrate the characteristics of the data over this test site, time series of daily averages of the brightness temperatures are constructed. These time series provide a useful pattern of annual variation of the AMSU-A measurements for intercalibration of microwave radiometers on board multiple satellites. To investigate the diurnal effect on the measurements, the time series of daily averaged brightness temperatures are constructed separately for the ascending and descending passes. Results show that there are little diurnal differences in measurements during the Antarctic winter months from each satellite. Therefore these measurements provide a practical approach to obtain relative channel biases of intersatellite data. The monthly averages of the measurements over July 2009 are employed to obtain the relative channel biases because it is the coldest month in Antarctica. The resultant channel biases for the three satellites are within the range of ±0.1 K for channels 1–5 and ±0.3 K for channels 6–15. This is strong evidence that Antarctica is a potentially good test site for intercalibration of microwave radiometers on board multiple satellites. The small relative biases at channels 1–5 indicate that Antarctica is a very stable test site that is particularly useful for intercalibration of measurements from the window channels. The establishment of a natural test site for calibration references is important for calibration and validation of spaceborne microwave instruments.

scholarly journals Development of a Time Series–Based Methodology for Estimation of Large-Area Soil Wetness over India Using IRS-P4 Microwave Radiometer Data

2005 ◽  
Vol 44 (1) ◽  
pp. 127-143 ◽  
Author(s):  
P. K. Thapliyal ◽  
P. K. Pal ◽  
M. S. Narayanan ◽  
J. Srinivasan
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Brightness Temperature ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Soil Conditions ◽  
Large Area ◽  
Brightness Temperatures ◽  
Boundary Parameter ◽  
Soil Wetness

Abstract Soil moisture is a very important boundary parameter in numerical weather prediction at different spatial and temporal scales. Satellite-based microwave radiometric observations are considered to be the best because of their high sensitivity to soil moisture, apart from possessing all-weather and day–night observation capabilities with high repetitousness. In the present study, 6.6-GHz horizontal-polarization brightness temperature data from the Multifrequency Scanning Microwave Radiometer (MSMR) onboard the Indian Remote Sensing Satellite IRS-P4 have been used for the estimation of large-area-averaged soil wetness. A methodology has been developed for the estimation of soil wetness for the period of June–July from the time series of MSMR brightness temperatures over India. Maximum and minimum brightness temperatures for each pixel are assigned to the driest and wettest periods, respectively. A daily soil wetness index over each pixel is computed by normalizing brightness temperature observations from these extreme values. This algorithm has the advantage that it takes into account the effect of time-invariant factors, such as vegetation, surface roughness, and soil characteristics, on soil wetness estimation. Weekly soil wetness maps compare well to corresponding weekly rainfall maps depicting clearly the regions of dry and wet soil conditions. Comparisons of MSMR-derived soil wetness with in situ observations show a high correlation (R > 0.75), with a standard error of the soil moisture estimate of less than 7% (volumetric unit) for the surface (0–5 cm) and subsurface (5–10 cm) soil moisture.

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