scholarly journals Assessment of New Satellite Missions within the Framework of Numerical Weather Prediction

2020 ◽  
Vol 12 (10) ◽  
pp. 1580 ◽  
Author(s):  
Stuart Newman ◽  
Fabien Carminati ◽  
Heather Lawrence ◽  
Niels Bormann ◽  
Kirsti Salonen ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Gap Analysis ◽  
Weather Prediction ◽  
Data Sets ◽  
Next Generation ◽  
Numerical Weather ◽  
Surface Emission ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Global Precipitation

Confidence in the use of Earth observations for monitoring essential climate variables (ECVs) relies on the validation of satellite calibration accuracy to within a well-defined uncertainty. The gap analysis for integrated atmospheric ECV climate monitoring (GAIA-CLIM) project investigated the calibration/validation of satellite data sets using non-satellite reference data. Here, we explore the role of numerical weather prediction (NWP) frameworks for the assessment of several meteorological satellite sensors: the advanced microwave scanning radiometer 2 (AMSR2), microwave humidity sounder-2 (MWHS-2), microwave radiation imager (MWRI), and global precipitation measurement (GPM) microwave imager (GMI). We find departures (observation-model differences) are sensitive to instrument calibration artefacts. Uncertainty in surface emission is identified as a key gap in our ability to validate microwave imagers quantitatively in NWP. The prospects for NWP-based validation of future instruments are considered, taking as examples the microwave sounder (MWS) and infrared atmospheric sounding interferometer-next generation (IASI-NG) on the next generation of European polar-orbiting satellites. Through comparisons with reference radiosondes, uncertainties in NWP fields can be estimated in terms of equivalent top-of-atmosphere brightness temperature. We find NWP-sonde differences are consistent with a total combined uncertainty of 0.15 K for selected temperature sounding channels, while uncertainties for humidity sounding channels typically exceed 1 K.

scholarly journals An assessment of Bayesian bias estimator for numerical weather prediction

2008 ◽  
Vol 15 (6) ◽  
pp. 1013-1022 ◽  
Author(s):  
J. Son ◽  
D. Hou ◽  
Z. Toth
Keyword(s):  
Time Series ◽  
Numerical Weather Prediction ◽  
Sampling Error ◽  
Weather Prediction ◽  
Training Data ◽  
Statistical Characteristics ◽  
Forecast Errors ◽  
Data Sets ◽  
Data Set ◽  
Numerical Weather

Abstract. Various statistical methods are used to process operational Numerical Weather Prediction (NWP) products with the aim of reducing forecast errors and they often require sufficiently large training data sets. Generating such a hindcast data set for this purpose can be costly and a well designed algorithm should be able to reduce the required size of these data sets. This issue is investigated with the relatively simple case of bias correction, by comparing a Bayesian algorithm of bias estimation with the conventionally used empirical method. As available forecast data sets are not large enough for a comprehensive test, synthetically generated time series representing the analysis (truth) and forecast are used to increase the sample size. Since these synthetic time series retained the statistical characteristics of the observations and operational NWP model output, the results of this study can be extended to real observation and forecasts and this is confirmed by a preliminary test with real data. By using the climatological mean and standard deviation of the meteorological variable in consideration and the statistical relationship between the forecast and the analysis, the Bayesian bias estimator outperforms the empirical approach in terms of the accuracy of the estimated bias, and it can reduce the required size of the training sample by a factor of 3. This advantage of the Bayesian approach is due to the fact that it is less liable to the sampling error in consecutive sampling. These results suggest that a carefully designed statistical procedure may reduce the need for the costly generation of large hindcast datasets.

scholarly journals EuLerian Identification of ascending Air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part I: Development of deep learning model

2021 ◽  
Author(s):  
Julian Francesco Quinting ◽  
Christian M. Grams
Keyword(s):  
Logistic Regression ◽  
Deep Learning ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Data Sets ◽  
Forecast Uncertainty ◽  
Numerical Weather ◽  
Process Oriented ◽  
Air Streams

Abstract. Physical processes on the synoptic scale are important modulators of the large-scale extratropical circulation. In particular, rapidly ascending air streams in extratropical cyclones, so-called warm conveyor belts (WCBs), modulate the upper-tropospheric Rossby wave pattern and are sources and magnifiers of forecast uncertainty. Thus, from a process-oriented perspective, numerical weather prediction (NWP) and climate models should adequately represent WCBs. The identification of WCBs usually involves Lagrangian air parcel trajectories that ascend from the lower to the upper troposphere within two days. This requires numerical data with high spatial and temporal resolution which is often not available from standard output and requires expensive computations. This study introduces a novel framework that aims to predict the footprints of the WCB inflow, ascent, and outflow stages over the Northern Hemisphere from instantaneous gridded fields using convolutional neural networks (CNNs). With its comparably low computational costs and relying on standard model output alone the new diagnostic enables the systematic investigation of WCBs in large data sets such as ensemble reforecast or climate model projections which are mostly not suited for trajectory calculations. Building on the insights from a logistic regression approach of a previous study, the CNNs are trained using a combination of meteorological parameters as predictors and trajectory-based WCB footprints as predictands. Validation of the networks against the trajectory-based data set confirms that the CNN models reliably replicate the climatological frequency of WCBs as well as their footprints at instantaneous time steps. The CNN models significantly outperform previously developed logistic regression models. Including time-lagged information on the occurrence of WCB ascent as a predictor for the inflow and outflow stages further improves the models' skill considerably. A companion study demonstrates versatile applications of the CNNs in different data sets including the verification of WCBs in ensemble forecasts. Overall, the diagnostic demonstrates how deep learning methods may be used to investigate the representation of weather systems and of their related processes in NWP and climate models in order to shed light on forecast uncertainty and systematic biases from a process-oriented perspective.

scholarly journals EuLerian Identification of Ascending air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part II: Model application to different data sets

2021 ◽  
Author(s):  
Julian Francesco Quinting ◽  
Christian Michael Grams ◽  
Annika Oertel ◽  
Moritz Pickl
Keyword(s):  
Temporal Resolution ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Future Research ◽  
Data Sets ◽  
Ensemble Forecasts ◽  
Numerical Weather ◽  
Spatio Temporal ◽  
Air Streams

Abstract. Warm conveyor belts (WCBs) affect the atmospheric dynamics in midlatitudes and are highly relevant for total and extreme precipitation in many parts of the extratropics. Thus, these air streams and their effect on midlatitude weather should be well represented in numerical weather prediction (NWP) and climate models. This study applies newly developed convolutional neural network (CNN) models which allow the identification of footprints of WCB inflow, ascent, and outflow from a limited number of predictor fields at comparably low spatio-temporal resolution. The goal of the study is to demonstrate the versatile applicability of the CNN models to different data sets and that their application yields qualitatively and quantitatively similar results as their trajectory-based counterpart which is most frequently used to objectively identify WCBs but requires data at higher spatio-temporal resolution which is often not available and is computationally more expensive. First, an application to reanalyses reveals that the well-known relationship between WCB ascent and extratropical cyclones as well as between WCB outflow and blocking anticyclones is also found for WCB footprints identified with the CNN models. Second, the application to Japanese 55-year reanalyses shows how the CNN models may be used to identify erroneous predictor fields that deteriorate the models' reliability. Third, a verification of WCBs in operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for three Northern Hemisphere winters reveals systematic biases over the North Atlantic with both the trajectory-based approach and the CNN models. The ensemble forecasts' skill tends to be lower when being evaluated with the trajectory approach due to the fine-scale structure of WCB footprints in comparison to the rather smooth CNN-based WCB footprints. A final example demonstrates the applicability of the CNN models to a convection permitting simulation with the ICOsahedral Nonhydrostatic (ICON) NWP model. Our study illustrates that deep learning methods can be used efficiently to support process-oriented understanding of forecast error and model biases, and opens numerous directions for future research.

scholarly journals Analysis of the Global Microwave Polarization Data of Clouds

2018 ◽  
Vol 32 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Xiping Zeng ◽  
Gail Skofronick-Jackson ◽  
Lin Tian ◽  
Amber E. Emory ◽  
William S. Olson ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Radar Data ◽  
Ice Crystals ◽  
Ice Particles ◽  
Precipitation Measurement ◽  
Wave Imaging ◽  
Microwave Imager ◽  
Climate Analysis ◽  
Microwave Radiometers ◽  
Global Precipitation

Abstract Information about the characteristics of ice particles in clouds is necessary for improving our understanding of the states, processes, and subsequent modeling of clouds and precipitation for numerical weather prediction and climate analysis. Two NASA passive microwave radiometers, the satellite-borne Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the aircraft-borne Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR), measure vertically and horizontally polarized microwaves emitted by clouds (including precipitating particles) and Earth’s surface below. In this paper, GMI (or CoSMIR) data are analyzed with CloudSat (or aircraft-borne radar) data to find polarized difference (PD) signals not affected by the surface, thereby obtaining the information on ice particles. Statistical analysis of 4 years of GMI and CloudSat data, for the first time, reveals that optically thick clouds contribute positively to GMI PD at 166 GHz over all the latitudes and their positive magnitude of 166-GHz GMI PD varies little with latitude. This result suggests that horizontally oriented ice crystals in thick clouds are common from the tropics to high latitudes, which contrasts the result of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that horizontally oriented ice crystals are rare in optically thin ice clouds.

scholarly journals Global Precipitation Forecasts by Merging Extrapolation-Based Nowcast and Numerical Weather Prediction with Locally Optimized Weights

2019 ◽  
Vol 34 (3) ◽  
pp. 701-714 ◽  
Author(s):  
Shunji Kotsuki ◽  
Kenta Kurosawa ◽  
Shigenori Otsuka ◽  
Koji Terasaki ◽  
Takemasa Miyoshi
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Precipitation Forecast ◽  
Numerical Weather ◽  
Optimal Weights ◽  
Threat Score ◽  
Optimal Weighting ◽  
Viable Approach ◽  
Global Precipitation

Abstract Over the past few decades, precipitation forecasts by numerical weather prediction (NWP) models have been remarkably improved. Yet, precipitation nowcasting based on spatiotemporal extrapolation tends to provide a better precipitation forecast at shorter lead times with much less computation. Therefore, merging the precipitation forecasts from the NWP and extrapolation systems would be a viable approach to quantitative precipitation forecast (QPF). Although the optimal weights between the NWP and extrapolation systems are usually defined as a global constant, the weights would vary in space, particularly for global QPF. This study proposes a method to find the optimal weights at each location using the local threat score (LTS), a spatially localized version of the threat score. We test the locally optimal weighting with a global NWP system composed of the local ensemble transform Kalman filter and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM-LETKF). For the extrapolation system, the RIKEN’s global precipitation nowcasting system called GSMaP_RNC is used. GSMaP_RNC extrapolates precipitation patterns from the Japan Aerospace Exploration Agency (JAXA)’s Global Satellite Mapping of Precipitation (GSMaP). The benefit of merging in global precipitation forecast lasts longer compared to regional precipitation forecast. The results show that the locally optimal weighting is beneficial.

scholarly journals Assessments of F16 Special Sensor Microwave Imager and Sounder Antenna Temperatures at Lower Atmospheric Sounding Channels

2009 ◽  
Vol 2009 ◽  
pp. 1-18 ◽  
Author(s):  
Banghua Yan ◽  
Fuzhong Weng
Keyword(s):  
Brightness Temperature ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Numerical Weather ◽  
Temperature Anomalies ◽  
Atmospheric Sounding ◽  
Microwave Imager

The main reflector of the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F-16 satellite emits variable radiation, and the SSMIS warm calibration load is intruded by direct and indirect solar radiation. These contamination sources produce antenna brightness temperature anomalies of around 2 K at SSMIS sounding channels which are obviously inappropriate for assimilation into numerical weather prediction models and remote sensing retrievals of atmospheric and surface parameters. In this study, antenna brightness temperature anomalies at several lower atmospheric sounding (LAS) channels are assessed, and the algorithm is developed for corrections of these antenna temperature anomalies. When compared against radiative transfer model simulations and simultaneous observations from AMSU-A aboard NOAA-16, the SSMIS antenna temperatures at 52.8, 53.6, 54.4, 55.5, 57.3, and 59.4 GHz after the anomaly correction exhibit small residual errors (<0.5 K). After such SSMIS antenna temperatures are applied to the National Center for Environmental Prediction Numerical Weather Prediction (NWP) model, more satellite data is used and the analysis field of the geopotential height is significantly improved throughout troposphere and lower stratosphere. Therefore, the SSMIS antenna temperatures after the anomaly correction have demonstrated their potentials in NWP models.

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