Assimilation of GOES-13 Imager Clear-Sky Water Vapor (6.5 μm) Radiances into a Warn-on-Forecast System

A prototype convection-allowing system using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model and employing an ensemble Kalman filter (EnKF) data assimilation technique has been developed and used during the spring 2016 and 2017 Hazardous Weather Testbeds. This system assimilates WSR-88D reflectivity and radial velocity, geostationary satellite cloud water path (CWP) retrievals, and available surface observations over a regional domain with a 3-km horizontal resolution at 15-min intervals, with 3-km initial conditions provided by an experimental High-Resolution Rapid Refresh ensemble (HRRR-e). However, no information on upper-level thermodynamic conditions in cloud-free regions is currently assimilated, as few timely observations exist. One potential solution is to also assimilate clear-sky satellite radiances, which provide information on mid- and upper-tropospheric temperature and moisture conditions. This research assimilates GOES-13 imager water vapor band (6.5 μm) radiances using the GSI-EnKF system to take advantage of the Community Radiative Transfer Model (CRTM) integration. Results using four cases from May 2016 showed that assimilating radiances generally had a neutral-to-positive impact on the model analysis, reducing humidity bias and/or errors at the appropriate model levels where verification observations were present. The effects on high-impact weather forecasts, as verified against forecast reflectivity and updraft helicity, were mixed. Three cases (9, 22, and 24 May) showed some improvement in skill, while the other (25 May) performed worse, despite the improved environment. This research represents the first step in designing a high-resolution ensemble data assimilation system to use GOES-16 Advanced Baseline Imager data, which provides additional water vapor bands and increased spatial and temporal resolution.

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Understanding Satellite-Observed Mountain-Wave Signatures Using High-Resolution Numerical Model Data

Weather and Forecasting ◽

10.1175/2008waf2222127.1 ◽

2009 ◽

Vol 24 (1) ◽

pp. 76-86 ◽

Cited By ~ 23

Author(s):

W. F. Feltz ◽

K. M. Bedka ◽

J. A. Otkin ◽

T. Greenwald ◽

S. A. Ackerman

Keyword(s):

Water Vapor ◽

High Resolution ◽

Weighting Function ◽

Model Simulation ◽

Wave Train ◽

Radiative Transfer Model ◽

Transfer Model ◽

Model Data ◽

Mountain Wave ◽

Mountain Waves

Abstract Prior work has shown that pilot reports of severe turbulence over Colorado often occur when complex interference or crossing wave patterns are present in satellite water vapor imagery downstream of the Rocky Mountains. To improve the understanding of these patterns, a high-resolution (1-km) Weather Research and Forecasting (WRF) model simulation was performed for an intense mountain-wave event that occurred on 6 March 2004. Synthetic satellite imagery was subsequently generated by passing the model-simulated data through a forward radiative transfer model. Comparison with concurrent Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor imagery demonstrates that the synthetic satellite data realistically captured many of the observed mesoscale features, including a mountain-wave train extending far downstream of the Colorado Front Range, the deformation of this wave train by an approaching cold front, and the substantially warmer brightness temperatures in the lee of the major mountain ranges composing the Colorado Rockies. Inspection of the model data revealed that the mountain waves redistributed the water vapor within the lower and middle troposphere, with the maximum column-integrated water vapor content occurring one-quarter wavelength downstream of the maximum ascent within each mountain wave. Due to this phase shift, the strongest vertical motions occur halfway between the locally warm and cool brightness temperature couplets in the water vapor imagery. Interference patterns seen in the water vapor imagery appear to be associated with mesoscale variability in the ambient wind field at or near mountaintop due to flow interaction with the complex topography. It is also demonstrated that the synergistic use of multiple water vapor channels provides a more thorough depiction of the vertical extent of the mountain waves since the weighting function for each channel peaks at a different height in the atmosphere.

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Clear-Sky Mask for the Advanced Clear-Sky Processor for Oceans

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2010jtecha1413.1 ◽

2010 ◽

Vol 27 (10) ◽

pp. 1609-1623 ◽

Cited By ~ 29

Author(s):

B. Petrenko ◽

A. Ignatov ◽

Y. Kihai ◽

A. Heidinger

Keyword(s):

High Resolution ◽

Radiative Transfer ◽

Weather Prediction ◽

Radiative Transfer Model ◽

Transfer Model ◽

Clear Sky ◽

Brightness Temperatures ◽

Spatial Uniformity ◽

Uniformity Test ◽

Cross Platform

Abstract The Advanced Clear Sky Processor for Oceans (ACSPO) generates clear-sky products, such as SST, clear-sky radiances, and aerosol, from Advanced Very High Resolution Radiometer (AVHRR)-like measurements. The ACSPO clear-sky mask (ACSM) identifies clear-sky pixels within the ACSPO products. This paper describes the ACSM structure and compares the performances of ACSM and its predecessor, Clouds from AVHRR Extended Algorithm (CLAVRx). ACSM essentially employs online clear-sky radiative transfer simulations enabled within ACSPO with the Community Radiative Transfer Model (CRTM) in conjunction with numerical weather prediction atmospheric [Global Forecast System (GFS)] and SST [Reynolds daily high-resolution blended SST (DSST)] fields. The baseline ACSM tests verify the accuracy of fitting observed brightness temperatures with CRTM, check retrieved SST for consistency with Reynolds SST, and identify ambient cloudiness at the boundaries of cloudy systems. Residual cloud effects are screened out with several tests, adopted from CLAVRx, and with the SST spatial uniformity test designed to minimize misclassification of sharp SST gradients as clouds. Cross-platform and temporal consistencies of retrieved SSTs are maintained by accounting for SST and brightness temperature biases, estimated within ACSPO online and independently from ACSM. The performance of ACSM is characterized in terms of statistics of deviations of retrieved SST from the DSST. ACSM increases the amount of “clear” pixels by 30% to 40% and improves statistics of retrieved SST compared with CLAVRx. ACSM is also shown to be capable of producing satisfactory statistics of SST anomalies if the reference SST field for the exact date of observations is unavailable at the time of processing.

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Effect of Out-of-Band Response in NOAA-16 AVHRR Channel 3b on Top-of-Atmosphere Radiances Calculated with the Community Radiative Transfer Model

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2009jtecha1259.1 ◽

2009 ◽

Vol 26 (9) ◽

pp. 1968-1972 ◽

Cited By ~ 8

Author(s):

Quanhua Liu ◽

Xingming Liang ◽

Yong Han ◽

Paul van Delst ◽

Yong Chen ◽

...

Keyword(s):

High Resolution ◽

Radiative Transfer ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Surface Wind ◽

Sea Surface ◽

Radiative Transfer Model ◽

Transfer Model ◽

Absorption Bands ◽

Clear Sky

Abstract The Community Radiative Transfer Model (CRTM) developed at the Joint Center for Satellite Data Assimilation (JCSDA) is used in conjunction with a daily sea surface temperature (SST) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) atmospheric data and surface wind to calculate clear-sky top-of-atmosphere (TOA) brightness temperatures (BTs) in three Advanced Very High Resolution Radiometer (AVHRR) thermal infrared channels over global oceans. CRTM calculations are routinely performed by the sea surface temperature team for four AVHRR instruments on board the National Oceanic and Atmospheric Administration (NOAA) satellites NOAA-16, NOAA-17, and NOAA-18 and the Meteorological Operation (MetOp) satellite MetOp-A, and they are compared with clear-sky TOA BTs produced by the operational AVHRR Clear-Sky Processor for Oceans (ACSPO). It was observed that the model minus observation (M−O) bias in the NOAA-16 AVHRR channel 3b (Ch3b) centered at 3.7 μm experienced a discontinuity of ∼0.3 K when a new CRTM version 1.1 (v.1.1) was implemented in ACSPO processing in September 2008. No anomalies occurred in any other AVHRR channel or for any other platform. This study shows that this discontinuity is caused by the out-of-band response in NOAA-16 AVHRR Ch3b and by using a single layer to the NCEP GFS temperature profiles above 10 hPa for the alpha version of CRTM. The problem has been solved in CRTM v.1.1, which uses one of the six standard atmospheres to fill in the missing data above the top pressure level in the input NCEP GFS data. It is found that, because of the out-of-band response, the NOAA-16 AVHRR Ch3b has sensitivity to atmospheric temperature at high altitudes. This analysis also helped to resolve another anomaly in the absorption bands of the High Resolution Infrared Radiation Sounder (HIRS) sensor, whose radiances and Jacobians were affected to a much greater extent by this CRTM inconsistency.

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A fast radiative transfer model for the assimilation of water vapor radiances from the Kalpana very high resolution radiometer

Geophysical Research Letters ◽

10.1029/2009gl037852 ◽

2009 ◽

Vol 36 (8) ◽

Cited By ~ 6

Author(s):

Randhir Singh ◽

Peter Rayer ◽

Roger Saunders ◽

Stefano Migliorini ◽

Roger Brugge ◽

...

Keyword(s):

Water Vapor ◽

High Resolution ◽

Radiative Transfer ◽

Radiative Transfer Model ◽

Transfer Model ◽

Very High

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The QME AERI LBLRTM: A Closure Experiment for Downwelling High Spectral Resolution Infrared Radiance

Journal of the Atmospheric Sciences ◽

10.1175/jas3300.1 ◽

2004 ◽

Vol 61 (22) ◽

pp. 2657-2675 ◽

Cited By ~ 84

Author(s):

D. D. Turner ◽

D. C. Tobin ◽

S. A. Clough ◽

P. D. Brown ◽

R. G. Ellingson ◽

...

Keyword(s):

Water Vapor ◽

Radiative Transfer ◽

High Spectral Resolution ◽

Radiative Transfer Model ◽

Transfer Model ◽

Continuum Absorption ◽

Clear Sky ◽

Infrared Radiance ◽

Wide Range ◽

Atmospheric State

Abstract Research funded by the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program has led to significant improvements in longwave radiative transfer modeling over the last decade. These improvements, which have generally come in small incremental changes, were made primarily in the water vapor self- and foreign-broadened continuum and the water vapor absorption line parameters. These changes, when taken as a whole, result in up to a 6 W m−2 improvement in the modeled clear-sky downwelling longwave radiative flux at the surface and significantly better agreement with spectral observations. This paper provides an overview of the history of ARM with regard to clear-sky longwave radiative transfer, and analyzes remaining related uncertainties in the ARM state-of-the-art Line-by-Line Radiative Transfer Model (LBLRTM). A quality measurement experiment (QME) for the downwelling infrared radiance at the ARM Southern Great Plains site has been ongoing since 1994. This experiment has three objectives: 1) to validate and improve the absorption models and spectral line parameters used in line-by-line radiative transfer models, 2) to assess the ability to define the atmospheric state, and 3) to assess the quality of the radiance observations that serve as ground truth for the model. Analysis of data from 1994 to 1997 made significant contributions to optimizing the QME, but is limited by small but significant uncertainties and deficiencies in the atmospheric state and radiance observations. This paper concentrates on the analysis of QME data from 1998 to 2001, wherein the data have been carefully selected to address the uncertainties in the 1994–97 dataset. Analysis of this newer dataset suggests that the representation of self-broadened water vapor continuum absorption is 3%–8% too strong in the 750–1000 cm−1 region. The dataset also provides information on the accuracy of the self- and foreign-broadened continuum absorption in the 1100–1300 cm−1 region. After accounting for these changes, remaining differences in modeled and observed downwelling clear-sky fluxes are less than 1.5 W m−2 over a wide range of atmospheric states.

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Homogeneity criteria within IASI pixels for the preparation of an all-sky assimilation

10.5194/amt-2018-288 ◽

2018 ◽

Author(s):

Imane Farouk ◽

Nadia Fourrie ◽

Vincent Guidard

Keyword(s):

Mesoscale Model ◽

Positive Impact ◽

Weather Prediction ◽

Liquid Water Content ◽

Current Data ◽

Radiative Transfer Model ◽

Transfer Model ◽

Cloud Detection ◽

Clear Sky ◽

The Impact

Abstract. This article focuses on a selection of satellite infra-red IASI observations and their simulation in the global Numerical Weather Prediction (NWP) system ARPEGE (Action de Recherche Petite Echelle Grande Echelle), using the sophisticated radiative transfer model RTTOV-CLD which takes into account the cloud multi-layers and the cloud scattering from atmospheric profiles and cloudy microphysical parameters (liquid water content, ice content and cloud fraction). The aim of this work is to select homogeneous scenes by using information of the collocated Advanced Very High Resolution Radiometer (AVHRR) pixels inside each IASI field of view and to retain the most favourable cases for the assimilation of IASI infrared radiances. Two methods to select homogeneous scenes using homogeneity criteria already proposed en the literature were employed; criteria derived from Martinet et al. (2013) for cloudy sky selection in the French mesoscale model AROME (Applications of Research to Operations at MEsoscale), and the criteria from Eresmaa (2014) for clear sky selection in the global model IFS (Integrated Forecasting System). An intercomparison between these methods reveals considerable differences, either in the method to compute the criteria or in the statistical results. From this comparison a revised method is proposed that is a compromise between the different tested methods, using the two infrared AVHRR channels to define the homogeneity criteria in the brightness temperature space. This revised method has a positive impact on the observation statistics minus the 15 simulation statistics, while retaining 36 % observations for the assimilation. It was then tested in the NWP system ARPEGE and tested for the clear-sky assimilation. These criteria were added to the current data selection based on the Mc Nally and Watts (2003) cloud detection. It appears that the impact on analyses and forecast is rather neutral.

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Homogeneity criteria from AVHRR information within IASI pixels in a numerical weather prediction context

Atmospheric Measurement Techniques ◽

10.5194/amt-12-3001-2019 ◽

2019 ◽

Vol 12 (6) ◽

pp. 3001-3017

Author(s):

Imane Farouk ◽

Nadia Fourrié ◽

Vincent Guidard

Keyword(s):

Numerical Weather Prediction ◽

Mesoscale Model ◽

Positive Impact ◽

Weather Prediction ◽

Radiative Transfer Model ◽

Transfer Model ◽

Detection Scheme ◽

Numerical Weather ◽

Clear Sky ◽

The Impact

Abstract. This article focuses on the selection of satellite infrared IASI (Infrared Atmospheric Sounding Interferometer) observations in the global numerical weather prediction (NWP) system ARPEGE (Action de Recherche Petite Echelle Grande Echelle). The observation simulation is performed with the sophisticated radiative transfer model RTTOV-CLD, which takes into account the cloud scattering and the multilayer clouds from atmospheric profiles and cloud microphysical profiles (liquid water content, ice content and cloud fraction). The aim of this work is to select homogeneous scenes by using the information of the collocated Advanced Very High Resolution Radiometer (AVHRR) pixels inside each IASI field of view and to retain the most favourable cases for the assimilation of IASI infrared radiances. Two methods to select homogeneous scenes using homogeneity criteria already proposed in the literature were adapted: the criteria derived from Martinet et al. (2013) for cloudy sky selection in the French mesoscale model AROME (Applications of Research to Operations at MEsoscale) and the criteria from Eresmaa (2014) for clear-sky selection in the global model IFS (Integrated Forecasting System). A comparison between these methods reveals considerable differences, in both the method to compute the criteria and the statistical results. From this comparison a revised method representing a kind of compromise between the different tested methods is proposed and it uses the two infrared AVHRR channels to define the homogeneity criteria in the brightness temperature space. This revised method has a positive impact on the observation minus the simulation statistics, while retaining 36 % of observations for the assimilation. It was then tested in the NWP system ARPEGE for the clear-sky assimilation. These criteria were added to the current data selection based on the McNally and Watts (2003) cloud detection scheme. It appears that the impact on analyses and forecasts is rather neutral.

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Long-Term Observations of Microwave Brightness Temperatures over a Metropolitan Area: Comparison of Radiometric Data and Spectra Simulated with the Use of Radiosonde Measurements

Remote Sensing ◽

10.3390/rs13112061 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2061

Author(s):

Mikhail V. Belikovich ◽

Mikhail Yu. Kulikov ◽

Dmitry S. Makarov ◽

Natalya K. Skalyga ◽

Vitaly G. Ryskin ◽

...

Keyword(s):

Radiative Transfer ◽

Elevation Angle ◽

Radiative Transfer Model ◽

Transfer Model ◽

Systematic Bias ◽

Tropospheric Temperature ◽

Absorption Model ◽

60 Ghz ◽

Error Budget ◽

The Difference

Ground-based microwave radiometers are increasingly used in operational meteorology and nowcasting. These instruments continuously measure the spectra of downwelling atmospheric radiation in the range 20–60 GHz used for the retrieval of tropospheric temperature and water vapor profiles. Spectroscopic uncertainty is an important part of the retrieval error budget, as it leads to systematic bias. In this study, we analyze the difference between observed and simulated microwave spectra obtained from more than four years of microwave and radiosonde observations over Nizhny Novgorod (56.2° N, 44° E). We focus on zenith-measured and elevation-scanning data in clear-sky conditions. The simulated spectra are calculated by a radiative transfer model with the use of radiosonde profiles and different absorption models, corresponding to the latest spectroscopy research. In the case of zenith-measurements, we found a systematic bias (up to ~2 K) of simulated spectra at 51–54 GHz. The sign of bias depends on the absorption model. A thorough investigation of the error budget points to a spectroscopic nature of the observed differences. The dependence of the results on the elevation angle and absorption model can be explained by the basic properties of radiative transfer and by cloud contamination at elevation angles.

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An Efficient Dual-Resolution Approach for Ensemble Data Assimilation and Tests with Simulated Doppler Radar Data

Monthly Weather Review ◽

10.1175/2007mwr2120.1 ◽

2008 ◽

Vol 136 (3) ◽

pp. 945-963 ◽

Cited By ~ 31

Author(s):

Jidong Gao ◽

Ming Xue

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Radial Velocity ◽

Computational Cost ◽

Radar Data ◽

Horizontal Resolution ◽

Velocity Data ◽

Background Error ◽

Cross Covariance ◽

Radial Velocity Data

Abstract A new efficient dual-resolution (DR) data assimilation algorithm is developed based on the ensemble Kalman filter (EnKF) method and tested using simulated radar radial velocity data for a supercell storm. Radar observations are assimilated on both high-resolution and lower-resolution grids using the EnKF algorithm with flow-dependent background error covariances estimated from the lower-resolution ensemble. It is shown that the flow-dependent and dynamically evolved background error covariances thus estimated are effective in producing quality analyses on the high-resolution grid. The DR method has the advantage of being able to significantly reduce the computational cost of the EnKF analysis. In the system, the lower-resolution ensemble provides the flow-dependent background error covariance, while the single-high-resolution forecast and analysis provides the benefit of higher resolution, which is important for resolving the internal structures of thunderstorms. The relative smoothness of the covariance obtained from the lower 4-km-resolution ensemble does not appear to significantly degrade the quality of analysis. This is because the cross covariance among different variables is of first-order importance for “retrieving” unobserved variables from the radar radial velocity data. For the DR analysis, an ensemble size of 40 appears to be a reasonable choice with the use of a 4-km horizontal resolution in the ensemble and a 1-km resolution in the high-resolution analysis. Several sensitivity tests show that the DR EnKF system is quite robust to different observation errors. A 4-km thinned data resolution is a compromise that is acceptable under the constraint of real-time applications. A data density of 8 km leads to a significant degradation in the analysis.

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A Satellite-Based Assessment of Upper-Tropospheric Water Vapor Measurements during AFWEX

Journal of Applied Meteorology and Climatology ◽

10.1175/2009jamc2250.1 ◽

2009 ◽

Vol 48 (11) ◽

pp. 2284-2294 ◽

Cited By ~ 5

Author(s):

Eui-Seok Chung ◽

Brian J. Soden

Keyword(s):

Water Vapor ◽

Radiative Transfer Model ◽

Transfer Model ◽

Raman Lidar ◽

Test Bed ◽

Upper Troposphere ◽

Brightness Temperatures ◽

Radiation Test ◽

Humidity Measurements ◽

Atmospheric Radiation Measurement

Abstract Consistency of upper-tropospheric water vapor measurements from a variety of state-of-the-art instruments was assessed using collocated Geostationary Operational Environmental Satellite-8 (GOES-8) 6.7-μm brightness temperatures as a common benchmark during the Atmospheric Radiation Measurement Program (ARM) First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX). To avoid uncertainties associated with the inversion of satellite-measured radiances into water vapor quantity, profiles of temperature and humidity observed from in situ, ground-based, and airborne instruments are inserted into a radiative transfer model to simulate the brightness temperature that the GOES-8 would have observed under those conditions (i.e., profile-to-radiance approach). Comparisons showed that Vaisala RS80-H radiosondes and Meteolabor Snow White chilled-mirror dewpoint hygrometers are systemically drier in the upper troposphere by ∼30%–40% relative to the GOES-8 measured upper-tropospheric humidity (UTH). By contrast, two ground-based Raman lidars (Cloud and Radiation Test Bed Raman lidar and scanning Raman lidar) and one airborne differential absorption lidar agree to within 10% of the GOES-8 measured UTH. These results indicate that upper-tropospheric water vapor can be monitored by these lidars and well-calibrated, stable geostationary satellites with an uncertainty of less than 10%, and that correction procedures are required to rectify the inherent deficiencies of humidity measurements in the upper troposphere from these radiosondes.

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