scholarly journals The Framework for Assimilating All-Sky GPM Microwave Imager Brightness Temperature Data in the NASA GEOS Data Assimilation System

2020 ◽  
Vol 148 (6) ◽  
pp. 2433-2455
Author(s):  
Min-Jeong Kim ◽  
Jianjun Jin ◽  
Amal El Akkraoui ◽  
Will McCarty ◽  
Ricardo Todling ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Brightness Temperature ◽  
Heavy Precipitation ◽  
Temperature Data ◽  
Background Error ◽  
Single Observation ◽  
Microwave Brightness Temperature ◽  
Earth Observing System ◽  
Microwave Imager ◽  
Non Gaussian

Abstract Satellite radiance observations combine global coverage with high temporal and spatial resolution, and bring vital information to NWP analyses especially in areas where conventional data are sparse. However, most satellite observations that are actively assimilated have been limited to clear-sky conditions due to difficulties associated with accounting for non-Gaussian error characteristics, nonlinearity, and the development of appropriate observation operators for cloud- and precipitation-affected satellite radiance data. This article provides an overview of the development of the Gridpoint Statistical Interpolation (GSI) configurations to assimilate all-sky data from microwave imagers such as the GPM Microwave Imager (GMI) in the NASA Goddard Earth Observing System (GEOS). Electromagnetic characteristics associated with their wavelengths allow microwave imager data to be highly sensitive to precipitation. Therefore, all-sky data assimilation efforts described in this study are primarily focused on utilizing these data in precipitating regions. To utilize data in cloudy and precipitating regions, state and analysis variables have been added for ice cloud, liquid cloud, rain, and snow. This required enhancing the observation operator to simulate radiances in heavy precipitation, including frozen precipitation. Background error covariances in both the central analysis and EnKF analysis in the GEOS hybrid 4D-EnVar system have been expanded to include hydrometeors. In addition, the bias correction scheme was enhanced to reduce biases associated with thick clouds and precipitation. The results from single observation experiments demonstrate the capability of assimilating all-sky microwave brightness temperature data in GEOS both when the model forecast produces excessive precipitation and too little precipitation. Additional experiments show that hydrometeors and dynamic variables such as winds and pressure are adjusted in physically consistent ways in response to the assimilation.

scholarly journals Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

2019 ◽  
Vol 11 (19) ◽  
pp. 2265 ◽  
Author(s):  
Yonghwan Kwon ◽  
Barton A. Forman ◽  
Jawairia A. Ahmad ◽  
Sujay V. Kumar ◽  
Yeosang Yoon
Keyword(s):  
Standard Deviation ◽  
Data Assimilation ◽  
Brightness Temperature ◽  
Liquid Water ◽  
Temperature Data ◽  
Passive Microwave ◽  
Support Vector ◽  
Surface Model ◽  
High Mountain ◽  
Microwave Brightness Temperature

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔTB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔTB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔTB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔTB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

scholarly journals Characterization of a hoar-development episode using SSM/I brightness temperatures in the vicinity of the GISP2 site, Greenland

1993 ◽  
Vol 17 ◽  
pp. 183-188 ◽  
Author(s):  
Christopher A. Shuman ◽  
Richard B. Alley ◽  
Sridhar Anandakrishnan
Keyword(s):  
Brightness Temperature ◽  
Surface Density ◽  
Temperature Data ◽  
Gradual Decline ◽  
Near Surface ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Microwave Imager ◽  
High Winds

Formation of a surface-hoar/depth-hoar complex at the GISP2 site in central Greenland was correlated with large changes in Special Sensor Microwave/Imager (SSM/I) brightness-temperature data. Pass-averaged SSM/I brightness-temperature data over a 1/2° latitude by 1° longitude cell for the 19 and 37 GHz, vertically (V) and horizontally (Η) polarized bands were manipulated to yield differential (V-Η) trends which clearly show a gradual decline as the hoar formation caused a progressively rougher surface with progressively lower density. The hoar episode ended as snowfall, and high winds buried and destroyed the surface-hoar layer and caused rapid V-Η increases in ≈ 1 day. Comparison of the different trends with changes in the field-monitored variables and theoretical values suggest that the V-Η trends are sensitive primarily to changes in surface roughness, and secondarily to near-surface density changes. Consistent expression of trends in microwave brightness temperature over 35 adjacent study cells indicates that this technique may provide a remote-sensing signature capable of defining the timing and spatial extent of surface- and depth-hoar formation in central Greenland.

scholarly journals Characterization of a hoar-development episode using SSM/I brightness temperatures in the vicinity of the GISP2 site, Greenland

1993 ◽  
Vol 17 ◽  
pp. 183-188 ◽  
Author(s):  
Christopher A. Shuman ◽  
Richard B. Alley ◽  
Sridhar Anandakrishnan
Keyword(s):  
Brightness Temperature ◽  
Surface Density ◽  
Temperature Data ◽  
Gradual Decline ◽  
Near Surface ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Microwave Imager ◽  
High Winds

Formation of a surface-hoar/depth-hoar complex at the GISP2 site in central Greenland was correlated with large changes in Special Sensor Microwave/Imager (SSM/I) brightness-temperature data. Pass-averaged SSM/I brightness-temperature data over a 1/2° latitude by 1° longitude cell for the 19 and 37 GHz, vertically (V) and horizontally (Η) polarized bands were manipulated to yield differential (V-Η) trends which clearly show a gradual decline as the hoar formation caused a progressively rougher surface with progressively lower density. The hoar episode ended as snowfall, and high winds buried and destroyed the surface-hoar layer and caused rapid V-Η increases in ≈ 1 day. Comparison of the different trends with changes in the field-monitored variables and theoretical values suggest that the V-Η trends are sensitive primarily to changes in surface roughness, and secondarily to near-surface density changes. Consistent expression of trends in microwave brightness temperature over 35 adjacent study cells indicates that this technique may provide a remote-sensing signature capable of defining the timing and spatial extent of surface- and depth-hoar formation in central Greenland.

scholarly journals Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Carolyn J. Merry ◽  
Don J. Cavalieri
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Correlated Data ◽  
Data Sets ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Scatter Plots ◽  
Microwave Imager ◽  
Highly Correlated ◽  
The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

scholarly journals Simulation of Monsoon Depressions Using WRF-VAR: Impact of Different Background Error Statistics and Lateral Boundary Conditions

2014 ◽  
Vol 142 (10) ◽  
pp. 3586-3613 ◽  
Author(s):  
A. Routray ◽  
S. C. Kar ◽  
P. Mali ◽  
K. Sowjanya
Keyword(s):  
Data Assimilation ◽  
Boundary Conditions ◽  
Initial Conditions ◽  
Variational Data Assimilation ◽  
Error Statistics ◽  
Background Error ◽  
Single Observation ◽  
Monsoon Depressions ◽  
Data Assimilation System ◽  
Assimilation System

Abstract In a variational data assimilation system, background error statistics (BES) spread the influence of the observations in space and filter analysis increments through dynamic balance or statistical relationships. In a data-sparse region such as the Bay of Bengal, BES play an important role in defining the location and structure of monsoon depressions (MDs). In this study, the Indian-region-specific BES have been computed for the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation system. A comparative study using single observation tests is carried out using the computed BES and global BES within the WRF system. Both sets of BES are used in the assimilation cycles and forecast runs for simulating the meteorological features associated with the MDs. Numerical experiments have been conducted to assess the relative impact of various BES in the analysis and simulations of the MDs. The results show that use of regional BES in the assimilation cycle has a positive impact on the prediction of the location, propagation, and development of rainbands associated with the MDs. The track errors of MDs are smaller when domain-specific BES are used in the assimilation cycle. Additional experiments have been conducted using data from the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) as initial and boundary conditions (IBCs) in the assimilation cycle. The results indicate that the use of domain-dependent BES and high-resolution ERA-I data as IBCs further improved the initial conditions for the model leading to better forecasts of the MDs.

Joint Assimilation of Surface Temperature and L-Band Microwave Brightness Temperature in Land Data Assimilation

2013 ◽  
Vol 12 (3) ◽  
pp. vzj2012.0072 ◽  
Author(s):  
Xujun Han ◽  
Harrie-Jan Hendricks Franssen ◽  
Xin Li ◽  
Yanlin Zhang ◽  
Carsten Montzka ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Brightness Temperature ◽  
Microwave Brightness Temperature ◽  
Land Data Assimilation ◽  
L Band

scholarly journals Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Carolyn J. Merry ◽  
Don J. Cavalieri
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Correlated Data ◽  
Data Sets ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Scatter Plots ◽  
Microwave Imager ◽  
Highly Correlated ◽  
The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

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