scholarly journals Understanding Atmospheric Motion Vector Vertical Representativity Using a Simulation Study and First-Guess Departure Statistics

2015 ◽  
Vol 54 (12) ◽  
pp. 2479-2500 ◽  
Author(s):  
Peter Lean ◽  
Stefano Migliorini ◽  
Graeme Kelly
Keyword(s):  
Simulation Study ◽  
Mesoscale Model ◽  
Motion Vector ◽  
Computational Cost ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Medium Level ◽  
Atmospheric Motion ◽  
Upper Level ◽  
Height Assignment

AbstractAtmospheric motion vectors (AMVs) have been produced for decades and remain an important source of wind information. Many studies have suggested that the traditional interpretation of AMVs as representative of the wind at cloud top is suboptimal and that they are more representative of the winds within the cloud. This paper investigates the vertical representativity of cloudy AMVs using both first-guess departure [observation − background (O − B)] statistics and the simulation-study technique. A state-of-the-art convection-permitting mesoscale model (“UKV”) is used in conjunction with a radiative transfer model and the Nowcasting Satellite Application Facility (NWCSAF) AMV package to produce synthetic AMVs over a 1-month period. The simulated upper-level AMVs suffered from large height-assignment errors uncharacteristic of those in reality; these issues were partially alleviated by using the model cloud top instead of the assigned height. In agreement with previous studies, both the simulated and real AMVs were found to have the closest fit to a layer mean of the model winds with the majority of the layer below the estimated cloud top. However, improvements in the fit between the AMVs and the model were also found by simply lowering the assigned height. A short NWP trial hinted that height reassignment might lead to short-range forecast improvements. The results of this study indicate that the simulation technique was able to match the usefulness of O − B statistics for AMVs associated with low- and medium-level clouds (albeit at a higher computational cost); however, challenges remain in the simulation of upper-level clouds.

scholarly journals 2018 Atmospheric Motion Vector (AMV) Intercomparison Study

2019 ◽  
Vol 11 (19) ◽  
pp. 2240 ◽  
Author(s):  
David Santek ◽  
Richard Dworak ◽  
Sharon Nebuda ◽  
Steve Wanzong ◽  
Régis Borde ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Motion Vector ◽  
Estimation Method ◽  
Japan Meteorological Agency ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
European Organization ◽  
Specific Configuration ◽  
Atmospheric Motion ◽  
Height Assignment

Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.

A Multilayer Upper-Boundary Condition for Longwave Radiative Flux to Correct Temperature Biases in a Mesoscale Model

2011 ◽  
Vol 139 (6) ◽  
pp. 1952-1959 ◽  
Author(s):  
Steven M. Cavallo ◽  
Jimy Dudhia ◽  
Chris Snyder
Keyword(s):  
Mesoscale Model ◽  
Potential Temperature ◽  
Wrf Model ◽  
Longwave Radiation ◽  
Radiative Transfer Model ◽  
Cold Bias ◽  
Transfer Model ◽  
Radiation Physics ◽  
Stratospheric Water Vapor ◽  
Upper Level

Abstract An upper-level cold bias in potential temperature tendencies of 10 K day−1, strongest at the top of the model, is observed in Weather Research and Forecasting (WRF) model forecasts. The bias originates from the Rapid Radiative Transfer Model longwave radiation physics scheme and can be reduced substantially by 1) modifying the treatment within the scheme by adding a multilayer buffer between the model top and top of the atmosphere and 2) constraining stratospheric water vapor to remain within the estimated climatology in the stratosphere. These changes reduce the longwave heating rate bias at the model top to ±0.5 K day−1. Corresponding bias reductions are also seen, particularly near the tropopause.

scholarly journals WRF Model Experiments on the Antarctic Atmosphere in Winter

2011 ◽  
Vol 139 (4) ◽  
pp. 1279-1291 ◽  
Author(s):  
Esa-Matti Tastula ◽  
Timo Vihma
Keyword(s):  
Boundary Layer ◽  
Surface Layer ◽  
Wind Speed ◽  
Surface Pressure ◽  
Mesoscale Model ◽  
Wrf Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Temperature Bias ◽  
Radiation Parameterizations

Abstract The standard and polar versions 3.1.1 of the Weather Research and Forecasting (WRF) model, both initialized by the 40-yr ECMWF Re-Analysis (ERA-40), were run in Antarctica for July 1998. Four different boundary layer–surface layer–radiation scheme combinations were used in the standard WRF. The model results were validated against observations of the 2-m temperature, surface pressure, and 10-m wind speed at 9 coastal and 2 inland stations. The best choice for boundary layer and radiation parameterizations of the standard WRF turned out to be the Yonsei University boundary layer scheme in conjunction with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) surface layer scheme and the Rapid Radiative Transfer Model for longwave radiation. The respective temperature bias was on the order of 3°C less than the biases obtained with the other combinations. Increasing the minimum value for eddy diffusivity did, however, improve the performance of the asymmetric convective scheme by 0.8°C. Averaged over the 11 stations, the error growths in 24-h forecasts were almost identical for the standard and Polar WRF, but in 9-day forecasts Polar WRF gave a smaller 2-m temperature bias. For the Vostok station, however, the standard WRF gave a less positively biased 24-h temperature forecast. On average, the polar version gave the least biased surface pressure simulation. The wind speed simulation was characterized by low correlation values, especially under weak winds and for stations surrounded by complex topography.

Evaluation of shortwave radiation fluxes in the multi-layer SPARTACUS-Urban scheme using DART

2021 ◽  
Author(s):  
Megan Stretton ◽  
William Morrison ◽  
Robin Hogan ◽  
Sue Grimmond
Keyword(s):  
Radiative Transfer ◽  
Urban Form ◽  
Climate Models ◽  
Weather Prediction ◽  
Computational Cost ◽  
Three Dimensional ◽  
Shortwave Radiation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Urban Structure

<p>The heterogenous structure of cities impacts radiative exchanges (e.g. albedo and heat storage). Numerical weather prediction (NWP) models often characterise the urban structure with an infinite street canyon – but this does not capture the three-dimensional urban form. SPARTACUS-Urban (SU) - a fast, multi-layer radiative transfer model designed for NWP - is evaluated using the explicit Discrete Anisotropic Radiative Transfer (DART) model for shortwave fluxes across several model domains – from a regular array of cubes to real cities .</p><p>SU agrees with DART (errors < 5.5% for all variables) when the SU assumptions of building distribution are fulfilled (e.g. randomly distribution). For real-world areas with pitched roofs, SU underestimates the albedo (< 10%) and shortwave transmission to the surface (< 15%), and overestimates wall-plus-roof absorption (9-27%), with errors increasing with solar zenith angle. SU should be beneficial to weather and climate models, as it allows more realistic urban form (cf. most schemes) without large increases in computational cost.</p>

scholarly journals A Novel Approach for Satellite-Based Turbulence Nowcasting for Aviation

2020 ◽  
Vol 12 (14) ◽  
pp. 2255
Author(s):  
Axel Barleben ◽  
Stéphane Haussler ◽  
Richard Müller ◽  
Matthias Jerg
Keyword(s):  
Weather Prediction ◽  
Motion Vector ◽  
The Novel ◽  
Air Turbulence ◽  
Novel Approach ◽  
Atmospheric Motion ◽  
Nwp Model ◽  
Height Assignment ◽  
Horizontal Grid ◽  
Assignment Scheme

The predictability of aviation turbulence is influenced by energy-intensive flow patterns that are significantly smaller than the horizontal grid scale of current numerical weather prediction (NWP) models. The parameterization of these subgrid scale (SGS) processes is possible by means of an additional prognostic equation for the temporal change of turbulence kinetic energy (TKE), whereby scale transfer terms are used. This turbulence scheme has been applied operationally for 5 years in the NWP model ICON (Icosahedral Nonhydrostatic). The most important of the source terms parameterizes the Kelvin–Helmholtz instability, better known as clear air turbulence. This shear term was subjected to a nowcasting technique, is calculated with satellite data, and shifted forward in time using motion based on optical flow estimates and atmospheric motion vector (AMV). The nowcasts include turbulence altitude as determined by an adapted height assignment scheme presented here. The case studies illustrate that the novel approach for satellite-based turbulence nowcasting is a supplement to the NWP models.

scholarly journals Sensitivity of the Track and Intensity Forecasts of Typhoon Megi (2010) to Satellite-Derived Atmospheric Motion Vectors with the Ensemble Kalman Filter

2012 ◽  
Vol 29 (12) ◽  
pp. 1794-1810 ◽  
Author(s):  
Chanh Q. Kieu ◽  
Nguyen Minh Truong ◽  
Hoang Thi Mai ◽  
Thanh Ngo-Duc
Keyword(s):  
Kalman Filter ◽  
Motion Vector ◽  
The Philippines ◽  
Intensity Change ◽  
Track Forecast ◽  
Recent Effort ◽  
Low Level ◽  
Sharp Turn ◽  
Atmospheric Motion ◽  
Upper Level

Abstract In this study, sensitivities of the track and intensity forecasts of Typhoon Megi (2010) to the Cooperative Institute for Meteorological Satellite Studies (CIMSS) University of Wisconsin satellite atmospheric motion vector (AMV) dataset are examined. Assimilation of the CIMSS AMV dataset using the local ensemble transform Kalman filter implemented in the Weather Research and Forecasting model shows that the AMV data can significantly improve the track forecast of Typhoon Megi, especially the sharp turn from west-northwest to north after crossing the Philippines. By broadening the western Pacific subtropical high to the west, the AMV data can help reduce the eastward bias of the track, thus steering the storm away inimical shear environment and facilitating its subsequent development. Further sensitivity experiments with separated assimilation of the low- to midlevel (800–300 hPa) and upper-level (300–100 hPa) AMV winds reveal that, despite the sparse distribution of the low-level AMV winds with most of the data points located in the periphery of Megi’s main circulation, the track forecasts tend to be more sensitive to the low-level than to the upper-level wind observations. This indicates that the far-field low-level observations can improve the large-scale environmental flow that storms are to move in, giving rise to a better representation of the steering flow and subsequent intensity change. While much of the recent effort in tropical cyclone research focuses on inner-core observations to improve the intensity forecast, the results in this study show that the peripheral observations outside the storm center could contribute considerably to the intensity and track forecasts and deserve attention for better typhoon forecast skills.

scholarly journals Millimeter-Wave Precipitation Retrievals and Observed-versus-Simulated Radiance Distributions: Sensitivity to Assumptions

2007 ◽  
Vol 64 (11) ◽  
pp. 3808-3826 ◽  
Author(s):  
Chinnawat Surussavadee ◽  
David H. Staelin
Keyword(s):  
Millimeter Wave ◽  
Mesoscale Model ◽  
Mie Scattering ◽  
Precipitation Rate ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Test Bed ◽  
Surface Precipitation ◽  
Mixing Ratios ◽  
Nwp Model

Abstract Brightness temperature histograms observed at 50–191 GHz by the Advanced Microwave Sounding Unit (AMSU) on operational NOAA satellites are shown to be consistent with predictions made using a mesoscale NWP model [the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)] and a radiative transfer model [TBSCAT/F(λ)] for a global set of 122 storms coincident with the AMSU observations. Observable discrepancies between the observed and modeled histograms occurred when 1) snow and graupel mixing ratios were increased more than 15% and 25%, respectively, or their altitudes increased more than ∼25 mb; 2) the density, F(λ), of equivalent Mie-scattering ice spheres increased more than 0.03 g cm−3; and 3) the two-stream ice scattering increased more than ∼1%. Using the same MM5/TBSCAT/F(λ) model, neural networks were developed to retrieve the following from AMSU and geostationary microwave satellites: hydrometeor water paths, 15-min average surface-precipitation rates, and cell-top altitudes, all with 15-km resolution. Simulated AMSU rms precipitation-rate retrieval accuracies ranged from 0.4 to 21 mm h−1 when grouped by octaves of MM5 precipitation rate between 0.1 and 64 mm h−1, and were ∼3.8 mm h−1 for the octave 4–8 mm h−1. AMSU and geostationary microwave (GEM) precipitation-rate retrieval accuracies for random 50–50 mixtures of profiles simulated with either the baseline or a modified-physics model were largely insensitive to changes in model physics that would be clearly evident in AMSU observations if real. This insensitivity of retrieval accuracies to model assumptions implies that MM5/TBSCAT/F(λ) simulations offer a useful test bed for evaluating alternative millimeter-wave satellite designs and methods for retrieval and assimilation, to the extent that surface effects are limited.

scholarly journals Evaluation of microwave radiances of GPM/GMI for the all-sky assimilation in RTTOV framework

2018 ◽  
Author(s):  
Rohit Mangla ◽  
Indu Jayaluxmi
Keyword(s):  
Goodness Of Fit ◽  
Mesoscale Model ◽  
Wrf Model ◽  
Dipole Approximation ◽  
Surface Fluxes ◽  
Sensor Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Goodness Of Fit Test ◽  
Spatially Correlated

Abstract. This study evaluates the all-sky GPM/GMI radiances towards assimilation in regional mesoscale model at 183 ± 7 GHz. The radiative transfer model (RTM) namely RTTOV-SCATT is used for the simulation of three tropical cyclones (hudhud, vardah and kyant respectively). Within the RTM, the performance of non-spherical Discrete Dipole Approximation (DDA) shapes (sector snowflake, 6-bullet rosette, block-column and thinplate) are evaluated. The input data used in RTTOV-SCATT includes vertical hydrometeor profiles, humidity and surface fluxes. In addition, the first guess simulations from Weather Research Forecast (WRF) model were executed at 15 km resolution using ERA-Interim reanalysis datasets. Results indicate that observed minus first guess (FG departures) are symmetric with DDA shapes. The normalized probability density function of FG departures shows large number of spatially correlated samples between clear-sky and poorly forecasted region. Quality control (QC) method was performed to eliminate large FG departures due to instrumental anomalies or poor forecast of clouds and precipitation. The goodness of fit test, h-statistics and skewness of observed and simulated distribution show optimum results for thinplate shape in all the convective events. We also tested the high resolution ERA-5 reanalysis datasets for the simulation of all-sky radiances using thinplate shape. Results illustrate a potential to integrate the GMI sensor data within a WRF data assimilation system.

scholarly journals Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

2019 ◽  
Vol 11 (17) ◽  
pp. 2054 ◽  
Author(s):  
Soo Min Oh ◽  
Régis Borde ◽  
Manuel Carranza ◽  
In-Chul Shin
Keyword(s):  
Pearson Correlation ◽  
Weather Prediction ◽  
Motion Vector ◽  
Inversion Layer ◽  
Retrieval Algorithm ◽  
Total Contribution ◽  
Sensitivity Tests ◽  
Vector Difference ◽  
Atmospheric Motion ◽  
Height Assignment

We derived an atmospheric motion vector (AMV) algorithm for the Geostationary Korea Multipurpose Satellite (GEO-KOMPSAT-2A; GK-2A) launched on 4 December 2018, using the Advanced Himawari Imager (AHI) onboard Himawari-8, which is very similar to the Advanced Meteorological Imager onboard GK-2A. This study clearly describes the main steps in our algorithm and optimizes it for the target box size and height assignment methods by comparing AMVs with numerical weather prediction (NWP) and rawinsonde profiles for July 2016 and January 2017. Target box size sensitivity tests were performed from 8 × 8 to 48 × 48 pixels for three infrared channels and from 16 × 16 to 96 × 96 pixels for one visible channel. The results show that the smaller box increases the speed, whereas the larger one slows the speed without quality control. The best target box sizes were found to be 16 × 16 for CH07, 08, and 13, and 48 × 48 pixels for CH03. Height assignment sensitivity tests were performed for several methods, such as the cross-correlation coefficient (CCC), equivalent blackbody temperature (EBBT), infrared/water vapor (IR/WV) intercept, and CO2 slicing methods for a cloudy target as well as normalized total contribution (NTC) and normalized total cumulative contribution (NTCC) for a clear-air target. For a cloudy target, the CCC method is influenced by the quality of the cloud’s top pressure. Better results were found when using EBBT and IR/WV intercept methods together rather than individually. Furthermore, CO2 slicing had the best statistics. For a clear-air target, the combined use of NTC and NTCC had the best statistics. Additionally, the mean vector difference, root-mean-square (RMS) vector difference, bias, and RMS error (RMSE) between GK-2A AMVs and NWP or rawinsonde were smaller by approximately 18.2% on average than in the case of the Communication, Ocean and Meteorology Satellite (COMS) AMVs. In addition, we verified the similarity between GK-2A and Meteosat Third Generation (MTG) AMVs using the AHI of Himawari-8 from 21 July 2016. This similarity can provide evidence that the GK-2A algorithm works properly because the GK-2A AMV algorithm borrows many methods of the MTG AMV algorithm for geostationary data and inversion layer corrections. The Pearson correlation coefficients in the speed, direction, and height of the prescribed GK-2A and MTG AMVs were larger than 0.97, and the corresponding bias/RMSE were0.07/2.19 m/s, 0.21/14.8°, and 2.61/62.9 hPa, respectively, considering common quality indicator with forecast (CQIF) > 80.

scholarly journals Identifying the Uncertainty in Determining Satellite-Derived Atmospheric Motion Vector Height Attribution

2009 ◽  
Vol 48 (3) ◽  
pp. 450-463 ◽  
Author(s):  
Christopher S. Velden ◽  
Kristopher M. Bedka
Keyword(s):  
Great Plains ◽  
Motion Vector ◽  
Vertical Wind Shear ◽  
Careful Analysis ◽  
Department Of Energy ◽  
Dominant Factor ◽  
The North ◽  
Assignment Method ◽  
Atmospheric Motion ◽  
Height Assignment

Abstract This study investigates the assignment of pressure heights to satellite-derived atmospheric motion vectors (AMVs), commonly known as cloud-drift and water vapor–motion winds. Large volumes of multispectral AMV datasets are compared with collocated rawinsonde wind profiles collected by the U.S. Department of Energy Atmospheric Radiation Measurement Program at three geographically disparate sites: the southern Great Plains, the North Slope of Alaska, and the tropical western Pacific Ocean. From a careful analysis of these comparisons, the authors estimate that mean AMV observation errors are ∼5–5.5 m s−1 and that vector height assignment is the dominant factor in AMV uncertainty, contributing up to 70% of the error. These comparisons also reveal that in most cases the RMS differences between matched AMVs and rawinsonde wind values are minimized if the rawinsonde values are averaged over specified layers. In other words, on average, the AMV values better correlate to a motion over a mean tropospheric layer rather than to a traditionally assigned discrete level. The height assignment behavioral characteristics are specifically identified according to AMV height (high cloud vs low cloud), type (spectral bands; clear vs cloudy), geolocation, height assignment method, and amount of environmental vertical wind shear present. The findings have potentially important implications for data assimilation of AMVs, and these are discussed.

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