Observation system simulation experiment for a L-band microwave radiometer over rough bare soil site: A first step towards brightness temperature assimilation

AbstractIn this study, an Observing System Simulation Experiment was used to examine how the assimilation of temperature, water vapor, and wind profiles from a potential array of ground-based remote sensing boundary layer profiling instruments impacts the accuracy of atmospheric analyses when using an ensemble Kalman filter data assimilation system. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). The case study tracked the evolution of several extratropical weather systems that occurred across the contiguous United States during 7–8 January 2008. Overall, the results demonstrate that using networks of high-quality temperature, wind, and moisture profile observations of the lower troposphere has the potential to improve the accuracy of wintertime atmospheric analyses over land. The impact of each profiling system was greatest in the lower and middle troposphere on the variables observed or retrieved by that instrument; however, some minor improvements also occurred in the unobserved variables and in the upper troposphere, particularly when RAM observations were assimilated. The best analysis overall was achieved when DWL wind profiles and temperature and moisture observations from the RAM, AERI, or MWR were assimilated simultaneously, which illustrates that both mass and momentum observations are necessary to improve the analysis accuracy.

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Ensemble Kalman filter data assimilation in a Babcock-Leighton solar dynamo model: An observation system simulation experiment for reconstructing meridional flow speed

Geophysical Research Letters ◽

10.1002/2014gl061077 ◽

2014 ◽

Vol 41 (15) ◽

pp. 5361-5369 ◽

Cited By ~ 17

Author(s):

Mausumi Dikpati ◽

Jeffrey L. Anderson ◽

Dhrubaditya Mitra

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Simulation Experiment ◽

System Simulation ◽

Meridional Flow ◽

Flow Speed ◽

Solar Dynamo ◽

Dynamo Model ◽

Observation System

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An observing system simulation experiment (OSSE)-based assessment of the retrieval of above-cloud temperature and water vapor using hyperspectral infrared sounder

10.5194/amt-2020-518 ◽

2021 ◽

Author(s):

Jing Feng ◽

Yi Huang ◽

Zhipeng Qu

Keyword(s):

Water Vapor ◽

Simulation Experiment ◽

System Simulation ◽

Atmospheric Temperature ◽

Observation System ◽

Atmospheric Conditions ◽

Retrieval Method ◽

Convective Storms ◽

Cloud Properties ◽

Mean Square Errors

Abstract. Measuring atmospheric conditions above convective storms is challenging. This study finds that the uncertainties in cloud properties near the top of deep convective clouds have a non-negligible impact on the TOA infrared radiances which cannot be fully eliminated by adopting a slab-cloud assumption. To overcome this issue, a synergetic retrieval method is developed. This method integrates the infrared hyperspectral observations with cloud measurements from active sensors to retrieve atmospheric temperature, water vapor, and cloud properties simultaneously. Using an observation system simulation experiment (OSSE), we found that the retrieval method is capable of detecting the spatial distribution of temperature and humidity anomalies above convective storms and reducing the root-mean-square-errors in temperature and column integrated water vapor by more than half.

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Development of an Ionospheric Numerical Assimilation Nowcast and Forecast System Based on Gauss-Markov Kalman Filter-an Observation System Simulation Experiment taking Example for China and Its Surrounding Area

Chinese Journal of Geophysics ◽

10.1002/cjg2.1488 ◽

2010 ◽

Vol 53 (2) ◽

pp. 209-217

Author(s):

Xin-An YUE ◽

Wei-Xing WAN ◽

Li-Bo LIU ◽

Bai-Qi NING ◽

Bi-Qiang ZHAO ◽

...

Keyword(s):

Kalman Filter ◽

Simulation Experiment ◽

System Simulation ◽

Observation System ◽

Surrounding Area ◽

Forecast System

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Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 2: Observation system simulation experiments

Atmospheric Chemistry and Physics ◽

10.5194/acp-10-6699-2010 ◽

2010 ◽

Vol 10 (14) ◽

pp. 6699-6709 ◽

Cited By ~ 2

Author(s):

D. Huang ◽

A. Gasiewski ◽

W. Wiscombe

Keyword(s):

Liquid Water ◽

System Simulation ◽

Microwave Radiometer ◽

Retrieval Algorithm ◽

Observation System ◽

Total Variation Regularization ◽

L1 Norm ◽

Simulation Experiments ◽

Cloud Liquid Water ◽

L2 Norm

Abstract. Part 1 of this research concluded that many conditions of the 2003 Wakasa Bay experiment were not optimal for the purpose of tomographic retrieval. Part 2 (this paper) then aims to find possible improvements to the mobile cloud tomography method using observation system simulation experiments. We demonstrate that the incorporation of the L1 norm total variation regularization in the tomographic retrieval algorithm better reproduces discontinuous structures than the widely used L2 norm Tikhonov regularization. The simulation experiments reveal that a typical ground-based mobile setup substantially outperforms an airborne one because the ground-based setup usually moves slower and has greater contrast in microwave brightness between clouds and the background. It is shown that, as expected, the error in the cloud tomography retrievals increases monotonically with both the radiometer noise level and the uncertainty in the estimate of background brightness temperature. It is also revealed that a lower speed of platform motion or a faster scanning radiometer results in more scan cycles and more overlap between the swaths of successive scan cycles, both of which help to improve the retrieval accuracy. The last factor examined is aircraft height. It is found that the optimal aircraft height is 0.5 to 1.0 km above the cloud top. To summarize, this research demonstrates the feasibility of tomographically retrieving the spatial structure of cloud liquid water using current microwave radiometric technology and provides several general guidelines to improve future field-based studies of cloud tomography.

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Potential soil moisture products from the aquarius radiometer and scatterometer using an observing system simulation experiment

Geoscientific Instrumentation Methods and Data Systems ◽

10.5194/gi-2-113-2013 ◽

2013 ◽

Vol 2 (1) ◽

pp. 113-120 ◽

Cited By ~ 6

Author(s):

Y. Luo ◽

X. Feng ◽

P. Houser ◽

V. Anantharaj ◽

X. Fan ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Simulation Experiment ◽

System Simulation ◽

Temporal Variations ◽

Vegetation Coverage ◽

Surface Model ◽

L Band ◽

Observing System Simulation Experiment ◽

Mean Square Errors

Abstract. Using an observing system simulation experiment (OSSE), we investigate the potential soil moisture retrieval capability of the National Aeronautics and Space Administration (NASA) Aquarius radiometer (L-band 1.413 GHz) and scatterometer (L-band, 1.260 GHz). We estimate potential errors in soil moisture retrievals and identify the sources that could cause those errors. The OSSE system includes (i) a land surface model in the NASA Land Information System, (ii) a radiative transfer and backscatter model, (iii) a realistic orbital sampling model, and (iv) an inverse soil moisture retrieval model. We execute the OSSE over a 1000 × 2200 km2 region in the central United States, including the Red and Arkansas river basins. Spatial distributions of soil moisture retrieved from the radiometer and scatterometer are close to the synthetic truth. High root mean square errors (RMSEs) of radiometer retrievals are found over the heavily vegetated regions, while large RMSEs of scatterometer retrievals are scattered over the entire domain. The temporal variations of soil moisture are realistically captured over a sparely vegetated region with correlations 0.98 and 0.63, and RMSEs 1.28% and 8.23% vol/vol for radiometer and scatterometer, respectively. Over the densely vegetated region, soil moisture exhibits larger temporal variation than the truth, leading to correlation 0.70 and 0.67, respectively, and RMSEs 9.49% and 6.09% vol/vol respectively. The domain-averaged correlations and RMSEs suggest that radiometer is more accurate than scatterometer in retrieving soil moisture. The analysis also demonstrates that the accuracy of the retrieved soil moisture is affected by vegetation coverage and spatial aggregation.

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