Evaluating Convective Initiation in High-Resolution Numerical Weather Prediction Models Using GOES-16 Infrared Brightness Temperatures

2021 ◽  
Vol 149 (4) ◽  
pp. 1153-1172
Author(s):  
David S. Henderson ◽  
Jason A. Otkin ◽  
John R. Mecikalski
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Cloud Water ◽  
Convective Initiation ◽  
Model Simulations ◽  
Brightness Temperatures ◽  
Cloud Characteristics ◽  
Verification Methods

AbstractThe evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top.

scholarly journals Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1842 ◽  
Author(s):  
Christof Lorenz ◽  
Carsten Montzka ◽  
Thomas Jagdhuber ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
High Resolution ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Brightness Temperatures ◽  
Climate Analysis

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.

scholarly journals A Comparison of Methods Used to Populate Neighborhood-Based Contingency Tables for High-Resolution Forecast Verification

2017 ◽  
Vol 32 (2) ◽  
pp. 733-741 ◽  
Author(s):  
Craig S. Schwartz
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Contingency Tables ◽  
Comparison Of Methods ◽  
Forecast Verification ◽  
Forecast Performance ◽  
Verification Methods ◽  
Numerical Weather Prediction Models ◽  
Forecast Quality

Abstract As high-resolution numerical weather prediction models are now commonplace, “neighborhood” verification metrics are regularly employed to evaluate forecast quality. These neighborhood approaches relax the requirement that perfect forecasts must match observations at the grid scale, contrasting traditional point-by-point verification methods. One recently proposed metric, the neighborhood equitable threat score, is calculated from 2 × 2 contingency tables that are populated within a neighborhood framework. However, the literature suggests three subtly different methods of populating neighborhood-based contingency tables. Thus, this work compares and contrasts these three variants and shows they yield statistically significantly different conclusions regarding forecast performance, illustrating that neighborhood-based contingency tables should be constructed carefully and transparently. Furthermore, this paper shows how two of the methods use inconsistent event definitions and suggests a “neighborhood maximum” approach be used to fill neighborhood-based contingency tables.

scholarly journals Validation of the First Years of GPM Operation over Cyprus

2018 ◽  
Vol 10 (10) ◽  
pp. 1520 ◽  
Author(s):  
Adrianos Retalis ◽  
Dimitris Katsanos ◽  
Filippos Tymvios ◽  
Silas Michaelides
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Monthly Basis ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
First Results ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

scholarly journals Sensitivity of Cloud-Resolving Simulations of Warm-Season Convection to Cloud Microphysics Parameterizations

2007 ◽  
Vol 135 (8) ◽  
pp. 2854-2868 ◽  
Author(s):  
Changhai Liu ◽  
Mitchell W. Moncrieff
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warm Season ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Fine Grid ◽  
Upper Level ◽  
Heating Profiles ◽  
Microphysical Parameterization

Abstract This paper investigates the effects of cloud microphysics parameterizations on simulations of warm-season precipitation at convection-permitting grid spacing. The objective is to assess the sensitivity of summertime convection predictions to the bulk microphysics parameterizations (BMPs) at fine-grid spacings applicable to the next generation of operational numerical weather prediction models. Four microphysical parameterization schemes are compared: simple ice (Dudhia), four-class mixed phase (Reisner et al.), Goddard five-class mixed phase (Tao and Simpson), and five-class mixed phase with graupel (Reisner et al.). The experimentation involves a 7-day episode (3–9 July 2003) of U.S. midsummer convection under moderate large-scale forcing. Overall, the precipitation coherency manifested as eastward-moving organized convection in the lee of the Rockies is insensitive to the choice of the microphysics schemes, and the latent heating profiles are also largely comparable among the BMPs. The upper-level condensate and cloudiness, upper-level radiative cooling/heating, and rainfall spectrum are the most sensitive, whereas the domain-mean rainfall rate and areal coverage display moderate sensitivity. Overall, the three mixed-phase schemes outperform the simple ice scheme, but a general conclusion about the degree of sophistication in the microphysics treatment and the performance is not achievable.

High-resolution model Verification Evaluation (HiVE). Part 1: Using neighbourhood techniques for the assessment of ocean model forecast skill

2020 ◽  
Author(s):  
Jan Maksymczuk ◽  
Ric Crocker ◽  
Marion Mittermaier ◽  
Christine Pequignet
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Ocean Model ◽  
Forecast Skill ◽  
Model Verification ◽  
Model Forecast ◽  
Verification Methods ◽  
Resolution Model ◽  
The Right ◽  
Spatial Verification

<div> <p>HiVE is a CMEMS funded collaboration between the atmospheric Numerical Weather Prediction (NWP) verification and the ocean community within the Met Office, aimed at demonstrating the use of spatial verification methods originally developed for the evaluation of high-resolution NWP forecasts, with CMEMS ocean model forecast products. Spatial verification methods provide more scale appropriate ways to better assess forecast characteristics and accuracy of km-scale forecasts, where the detail looks realistic but may not be in the right place at the right time. As a result, it can be the case that coarser resolution forecasts verify better (e.g. lower root-mean-square-error) than the higher resolution forecast. In this instance the smoothness of the coarser resolution forecast is rewarded, though the higher-resolution forecast may be better. The project utilised open source code library known as Model Evaluation Toolkit (MET) developed at the US National Center for Atmospheric Research. </p> </div><div> <p> </p> </div><div> <p>This project saw, for the first time, the application of spatial verification methods to sub-10 km resolution ocean model forecasts. The project consisted of two parts. Part 1 describes an assessment of the forecast skill for SST of CMEMS model configurations at observing locations using an approach called HiRA (High Resolution Assessment). Part 2 is described in the companion poster to this one.  </p> </div><div> <p> </p> </div><div> <p>HiRA is a single-observation-forecast-neighbourhood-type method which makes use of commonly used ensemble verification metrics such as the Brier Score (BS) and the Continuous Ranked Probability Score (CRPS). In this instance all model grid points within a predefined neighbourhood of the observing location are considered equi-probable outcomes (or pseudo-ensemble members) at the observing location. The technique allows for an inter-comparison of models with different grid resolutions as well as between deterministic and probabilistic forecasts in an equitable and consistent way. In this work it has been applied to the CMEMS products delivered from the AMM7 (~7km) and AMM15 (~1.5km) model configurations for the European North West Shelf that are provided by the Met Office. </p> </div><div> <p> </p> </div><div> <p>It has been found that when neighbourhoods of equivalent extent are compared for both configurations it is possible to show improved forecast skill for SST for the higher resolution AMM15 both on- and off-shelf, which has been difficult to demonstrate previously using traditional metrics. Forecast skill generally degrades with increasing lead time for both configurations, with the off-shelf results for the higher resolution model showing increasing benefits over the coarser configuration. </p> </div>

scholarly journals Long-range transport of terrain-induced turbulence from high-resolution numerical simulations

2011 ◽  
Vol 11 (22) ◽  
pp. 11793-11805 ◽  
Author(s):  
M. Katurji ◽  
S. Zhong ◽  
P. Zawar-Reza
Keyword(s):  
High Resolution ◽  
Aspect Ratio ◽  
Numerical Simulations ◽  
Long Range ◽  
Prediction Models ◽  
Atmospheric Transport ◽  
Weather Prediction ◽  
Measurement Technology ◽  
Topographic Features ◽  
Range Transport

Abstract. Over complex terrain, an important question is how various topographic features may generate or alter wind turbulence and how far the influence can be extended downstream. Current measurement technology limits the capability in providing a long-range snapshot of turbulence as atmospheric eddies travel over terrain, interact with each other, change their productive and dissipative properties, and are then observed tens of kilometers downstream of their source. In this study, we investigate through high-resolution numerical simulations the atmospheric transport of terrain-generated turbulence in an atmosphere that is neutrally stratified. The simulations are two-dimensional with an isotropic spatial resolution of 15 m and run to a quasi-steady state. They are designed in such a way to allow an examination of the effects of a bell-shaped experimental hill with varying height and aspect ratio on turbulence properties generated by another hill 20 km upstream. Averaged fields of the turbulent kinetic energy (TKE) imply that terrain could have a large influence on velocity perturbations at least 30H (H is the terrain height) upstream and downstream of the terrain, with the largest effect happening in the area of the largest pressure perturbations. The results also show that downstream of the terrain the TKE fields are sensitive to the terrain's aspect ratio with larger enhancement in turbulence by higher aspect ratio, while upstream there is a suppression of turbulence that does not appear to be sensitive to the terrain aspect ratio. Instantaneous vorticity fields shows very detailed flow structures that resemble a multitude of eddy scales dynamically interacting while shearing oppositely paired vortices. The knowledge of the turbulence production and modifications by topography from these high-resolution simulations can be helpful in understanding long-range terrain-induced turbulence and improving turbulence parameterizations used in lower resolution weather prediction models.

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

2010 ◽  
Vol 27 (10) ◽  
pp. 1609-1623 ◽  
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.

scholarly journals Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Swagata Payra ◽  
Manju Mohan
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Winter Season ◽  
Wrf Model ◽  
Onset Time ◽  
Northwestern Part ◽  
Model Simulations ◽  
Western Disturbances ◽  
Promising Tool ◽  
Local Perturbations

The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD) approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF) model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.

Toward Better Understanding of the Contiguous Rain Area (CRA) Method for Spatial Forecast Verification

2009 ◽  
Vol 24 (5) ◽  
pp. 1401-1415 ◽  
Author(s):  
Elizabeth E. Ebert ◽  
William A. Gallus
Keyword(s):  
Correlation Coefficient ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Forecast Verification ◽  
Rain Area ◽  
Squared Error ◽  
Verification Methods ◽  
The Mean

Abstract The contiguous rain area (CRA) method for spatial forecast verification is a features-based approach that evaluates the properties of forecast rain systems, namely, their location, size, intensity, and finescale pattern. It is one of many recently developed spatial verification approaches that are being evaluated as part of a Spatial Forecast Verification Methods Intercomparison Project. To better understand the strengths and weaknesses of the CRA method, it has been tested here on a set of idealized geometric and perturbed forecasts with known errors, as well as nine precipitation forecasts from three high-resolution numerical weather prediction models. The CRA method was able to identify the known errors for the geometric forecasts, but only after a modification was introduced to allow nonoverlapping forecast and observed features to be matched. For the perturbed cases in which a radar rain field was spatially translated and amplified to simulate forecast errors, the CRA method also reproduced the known errors except when a high-intensity threshold was used to define the CRA (≥10 mm h−1) and a large translation error was imposed (>200 km). The decomposition of total error into displacement, volume, and pattern components reflected the source of the error almost all of the time when a mean squared error formulation was used, but not necessarily when a correlation-based formulation was used. When applied to real forecasts, the CRA method gave similar results when either best-fit criteria, minimization of the mean squared error, or maximization of the correlation coefficient, was chosen for matching forecast and observed features. The diagnosed displacement error was somewhat sensitive to the choice of search distance. Of the many diagnostics produced by this method, the errors in the mean and peak rain rate between the forecast and observed features showed the best correspondence with subjective evaluations of the forecasts, while the spatial correlation coefficient (after matching) did not reflect the subjective judgments.

scholarly journals The role of urban boundary layer investigated by high resolution models and ground based observations in Rome area: a step for understanding parameterizations potentialities

2013 ◽  
Vol 6 (3) ◽  
pp. 5297-5344
Author(s):  
E. Pichelli ◽  
R. Ferretti ◽  
M. Cacciani ◽  
A. M. Siani ◽  
V. Ciardini ◽  
...  
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Urban Areas ◽  
Prediction Models ◽  
Weather Prediction ◽  
Sonic Anemometer ◽  
Lower Atmosphere ◽  
Area Of Interest ◽  
The Impact

Abstract. The urban forcing on thermo-dynamical conditions can largely influences local evolution of the atmospheric boundary layer. Urban heat storage can produce noteworthy mesoscale perturbations of the lower atmosphere. The new generations of high-resolution numerical weather prediction models (NWP) is nowadays largely applied also to urban areas. It is therefore critical to reproduce correctly the urban forcing which turns in variations of wind, temperature and water vapor content of the planetary boundary layer (PBL). WRF-ARW, a new model generation, has been used to reproduce the circulation in the urban area of Rome. A sensitivity study is performed using different PBL and surface schemes. The significant role of the surface forcing in the PBL evolution has been verified by comparing model results with observations coming from many instruments (LiDAR, SODAR, sonic anemometer and surface stations). The crucial role of a correct urban representation has been demonstrated by testing the impact of different urban canopy models (UCM) on the forecast. Only one of three meteorological events studied will be presented, chosen as statistically relevant for the area of interest. The WRF-ARW model shows a tendency to overestimate vertical transmission of horizontal momentum from upper levels to low atmosphere, that is partially corrected by local PBL scheme coupled with an advanced UCM. Depending on background meteorological scenario, WRF-ARW shows an opposite behavior in correctly representing canopy layer and upper levels when local and non local PBL are compared. Moreover a tendency of the model in largely underestimating vertical motions has been verified.

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