scholarly journals Ensemble Mean Storm-Scale Performance in the Presence of Nonlinearity

2015 ◽  
Vol 143 (12) ◽  
pp. 5115-5133 ◽  
Author(s):  
Michael A. Hollan ◽  
Brian C. Ancell
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wrf Model ◽  
Forecast Errors ◽  
Ensemble Mean ◽  
Deterministic Solution ◽  
Significant Divergence ◽  
The Mean ◽  
And Control

Abstract The use of ensembles in numerical weather prediction models is becoming an increasingly effective method of forecasting. Many studies have shown that using the mean of an ensemble as a deterministic solution produces the most accurate forecasts. However, the mean will eventually lose its usefulness as a deterministic forecast in the presence of nonlinearity. At synoptic scales, this appears to occur between 12- and 24-h forecast time, and on storm scales it may occur significantly faster due to stronger nonlinearity. When this does occur, the question then becomes the following: Should the mean still be adhered to, or would a different approach produce better results? This paper will investigate the usefulness of the mean within a WRF Model utilizing an ensemble Kalman filter for severe convective events. To determine when the mean becomes unrealistic, the divergence of the mean of the ensemble (“mean”) and a deterministic forecast initialized from a set of mean initial conditions (“control”) are examined. It is found that significant divergence between the mean and control emerges no later than 6 h into a convective event. The mean and control are each compared to observations, with the control being more accurate for nearly all forecasts studied. For the case where the mean provides a better forecast than the control, an approach is offered to identify the member or group of members that is closest to the mean. Such a forecast will contain similar forecast errors as the mean, but unlike the mean, will be on an actual forecast trajectory.

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.

 Evaluation of high resolution WRF forecasts for Extreme Rainfall Events over Karnataka against high density in-situ observations 

2021 ◽  
Author(s):  
Ajay Bankar ◽  
Rakesh Vasudevan
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Distinct Advantage ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Synoptic Conditions

<p><span>Extreme Rainfall Events (EREs) in India has increased many folds in recent decades. These severe weather events are generally destructive in nature causing flash floods, catastrophic loss of life and property over densely populated urban cities. Various cities in Karnataka, a southern state in India, witnessed many EREs recently. Appropriate advanced warning systems to predict these events are crucial for preparedness of mitigation strategy to reduce human casualty and socio economic loss. Mesoscale models are essential tools for developing an integrated platform for disaster warning and management. From a stakeholder/user pint of view, primary requirement to tackle ERE related damages is accurate prediction of the observed rainfall location, coverage and intensity in advance. Weather prediction models have inherent limitations imposed primarily by approximations in the model and inadequacies in data. Hence, it is important to evaluate the skill of these models for many cases under different synoptic conditions to quantify model skill before using them for operational applications. The objective of the study is to evaluate performance of the Weather Research and Forecasting (WRF) model for several ERE cases in Karnataka at different model initial conditions. The EREs were identified from the distribution of rainfall events over different regions in Karnataka and those events comes under 1% probability were considered. We examined 38 ERE’s distributed over Karnataka for the period June to November for the years 2015-2019. WRF model is configured with 3 nested domains with outer, inner and innermost domains having resolution of 12 km, 9 km and 3 km respectively. Two sets of simulations are conducted in this study, i) staring at 12 hours prior to the ERE day (i.e. -1200 UTC) & ii) starting at 0000 UTC of the ERE day. Performance of the WRF model forecast is validated against 15 minutes rainfall observations from ~6000 rain gauge stations over Karnataka. During initial hours forecasts initiated at 1200 UTC has distinct advantage in terms of accuracy compared to those initiated at 0000 UTC for most of the cases. In general, model underpredict EREs and underprediction is relatively low for forecasts initiated at 12 00 UTC.</span></p>

Assessing Predictability with a Local Ensemble Kalman Filter

2007 ◽  
Vol 64 (4) ◽  
pp. 1116-1140 ◽  
Author(s):  
David Kuhl ◽  
Istvan Szunyogh ◽  
Eric J. Kostelich ◽  
Gyorgyi Gyarmati ◽  
D. J. Patil ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Data Assimilation Scheme ◽  
Ensemble Mean ◽  
Assimilation Scheme

Abstract In this paper, the spatiotemporally changing nature of predictability is studied in a reduced-resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), a state-of-the-art numerical weather prediction model. Atmospheric predictability is assessed in the perfect model scenario for which forecast uncertainties are entirely due to uncertainties in the estimates of the initial states. Uncertain initial conditions (analyses) are obtained by assimilating simulated noisy vertical soundings of the “true” atmospheric states with the local ensemble Kalman filter (LEKF) data assimilation scheme. This data assimilation scheme provides an ensemble of initial conditions. The ensemble mean defines the initial condition of 5-day deterministic model forecasts, while the time-evolved members of the ensemble provide an estimate of the evolving forecast uncertainties. The observations are randomly distributed in space to ensure that the geographical distribution of the analysis and forecast errors reflect predictability limits due to the model dynamics and are not affected by inhomogeneities of the observational coverage. Analysis and forecast error statistics are calculated for the deterministic forecasts. It is found that short-term forecast errors tend to grow exponentially in the extratropics and linearly in the Tropics. The behavior of the ensemble is explained by using the ensemble dimension (E dimension), a spatiotemporally evolving measure of the evenness of the distribution of the variance between the principal components of the ensemble-based forecast error covariance matrix. It is shown that in the extratropics the largest forecast errors occur for the smallest E dimensions. Since a low value of the E dimension guarantees that the ensemble can capture a large portion of the forecast error, the larger the forecast error the more certain that the ensemble can fully capture the forecast error. In particular, in regions of low E dimension, ensemble averaging is an efficient error filter and the ensemble spread provides an accurate prediction of the upper bound of the error in the ensemble-mean forecast.

scholarly journals Model error in weather forecasting

2001 ◽  
Vol 8 (6) ◽  
pp. 357-371 ◽  
Author(s):  
D. Orrell ◽  
L. Smith ◽  
J. Barkmeijer ◽  
T. N. Palmer
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Model Error ◽  
Local Model ◽  
State Dependent ◽  
Rapid Divergence ◽  
Dependent Model

Abstract. Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weather prediction models. A simple law is derived to relate model error to likely shadowing performance (how long the model can stay close to the observations). Imperfect model experiments are used to contrast the performance of truncated models relative to a high resolution run, and the operational model relative to the analysis. In both cases the component of forecast error due to state-dependent model error tends to grow as the square-root of forecast time, and provides a major source of error out to three days. These initial results suggest that model error plays a major role and calls for further research in quantifying both the local model drift and expected shadowing times.

scholarly journals Understanding the relationship between clouds and surface downward radiation forecast errors with Unsupervised Deep Learning

2021 ◽  
Author(s):  
Matthias Zech ◽  
Lueder von Bremen
Keyword(s):  
Deep Learning ◽  
Unsupervised Learning ◽  
Prediction Models ◽  
Weather Prediction ◽  
Reanalysis Data ◽  
Cloud Formation ◽  
Trade Wind ◽  
Forecast Errors ◽  
Model Learning ◽  
Unsupervised Deep Learning

<p>Cloudiness is a difficult parameter to forecast and has improved relatively little over the last decade in numerical weather prediction models as the EMCWF IFS. However, surface downward solar radiation forecast (ssrd) errors are becoming more important with higher penetration of photovoltaics in Europe as forecasts errors induce power imbalances that might lead to high balancing costs. This study continues recent approaches to better understand clouds using satellite images with Deep Learning. Unlike other studies which focus on shallow trade wind cumulus clouds over the ocean, this study investigates the European land area. To better understand the clouds, we use the daily MODIS optical cloud thickness product which shows both water and ice phase of the cloud. This allows to consider both cloud structure and cloud formation during learning. It is also much easier to distinguish between snow and cloud in contrast to using visible bands. Methodologically, it uses the Unsupervised Learning approach <em>tile2vec</em> to derive a lower dimensional representation of the clouds. Three cloud regions with two similar neighboring tiles and one tile from a different time and location are sampled to learn lower-rank embeddings. In contrast to the initial <em>tile2vec</em> implementation, this study does not sample arbitrarily distant tiles but uses the fractal dimension of the clouds in a pseudo-random sampling fashion to improve model learning.</p><p>The usefulness of the cloud segments is shown by applying them in a case study to investigate statistical properties of ssrd forecast errors over Europe which are derived from hourly ECMWF IFS forecasts and ERA5 reanalysis data. This study shows how Unsupervised Learning has high potential despite its relatively low usage compared to Supervised Learning in academia. It further shows, how the generated land cloud product can be used to better characterize ssrd forecast errors over Europe.</p>

scholarly journals Spatial Growth of Perturbations in a Turbulent Baroclinic Jet

2011 ◽  
Vol 68 (11) ◽  
pp. 2731-2741
Author(s):  
Rahul B. Mahajan ◽  
Gregory J. Hakim
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Storm Track ◽  
Spreading Rate ◽  
Forecast Errors ◽  
Far Field ◽  
Quasigeostrophic Model ◽  
Spreading Rates ◽  
Analysis Errors ◽  
Numerical Weather Prediction Models

Abstract The spatial spreading of infinitesimal disturbances superposed on a turbulent baroclinic jet is explored. This configuration is representative of analysis errors in an idealized midlatitude storm track and the insight gained may be helpful to understand the spreading of forecast errors in numerical weather prediction models. This problem is explored through numerical experiments of a turbulent baroclinic jet that is perturbed with spatially localized disturbances. Solutions from a quasigeostrophic model for the disturbance fields are compared with those for a passive tracer to determine whether disturbances propagate faster than the basic-state flow. Results show that the disturbance spreading rate is sensitive to the structure of the initial disturbance. Disturbances that are localized in potential vorticity (PV) have far-field winds that allow the disturbance to travel downstream faster than disturbances that are initially localized in geopotential, which have no far-field wind. Near the jet, the spread of the disturbance field is observed to exceed the tracer field for PV-localized disturbances, but not for the geopotential-localized disturbances. Spreading rates faster than the flow for geopotential-localized disturbances are found to occur only for disturbances located off the jet axis. These results are compared with those for zonal and time-independent jets to qualitatively assess the effects of transience and nonlinearity. This comparison suggests that the average properties of localized perturbations to the turbulent jet can be decomposed into a superposition of dynamics associated with a time-independent parallel flow plus a “diffusion” process.

On the effective resolution of WRF simulations at microscale grid resolution.

2021 ◽  
Author(s):  
Pedro Bolgiani ◽  
Javier Díaz-Fernández ◽  
Lara Quitián-Hernández ◽  
Mariano Sastre ◽  
Daniel Santos-Muñoz ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Research Community ◽  
Weather Research And Forecasting ◽  
Large Uncertainty ◽  
Partial Explanation ◽  
Synoptic Conditions ◽  
Large Effort ◽  
Numerical Weather Prediction Models

<p>As the computational capacity has been largely improved in the last decades, the grid configuration of numerical weather prediction models has stepped into microscale resolutions. Even if mesoscale models are not originally designed to reproduce fine scale phenomena, a large effort is being made by the research community to improve and adapt these systems. However, reasonable doubts exist regarding the ability of the models to forecast this type of events, due to the unfit parametrizations and the appearance of instabilities and lack of sensitivity in the variables. Here, the Weather Research and Forecasting (WRF) model effective resolution is evaluated for several situations and grid resolutions. This is achieved by assessing the curve of dissipation for the wind kinetic energy. Results show that the simulated energy spectrum responds to different synoptic conditions. Nevertheless, when the model is forced into microscale grid resolutions the dissipation curves present an unrealistic atmospheric energy. This may be a partial explanation to the aforementioned issues and imposes a large uncertainty to forecasting at these resolutions.</p>

scholarly journals Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

2014 ◽  
Vol 21 (5) ◽  
pp. 1027-1041 ◽  
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

Estimation of the Minimum Canopy Resistance for Croplands and Grasslands Using Data from the 2002 International H2O Project

2008 ◽  
Vol 136 (11) ◽  
pp. 4452-4469 ◽  
Author(s):  
Joseph G. Alfieri ◽  
Dev Niyogi ◽  
Peter D. Blanken ◽  
Fei Chen ◽  
Margaret A. LeMone ◽  
...  
Keyword(s):  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Moisture Flux ◽  
Mean Value ◽  
Surface Model ◽  
Canopy Resistance ◽  
Land Surface Models ◽  
Surface Models ◽  
The Mean

Abstract Vegetated surfaces, such as grasslands and croplands, constitute a significant portion of the earth’s surface and play an important role in land–atmosphere exchange processes. This study focuses on one important parameter used in describing the exchange of moisture from vegetated surfaces: the minimum canopy resistance (rcmin). This parameter is used in the Jarvis canopy resistance scheme that is incorporated into the Noah and many other land surface models. By using an inverted form of the Jarvis scheme, rcmin is determined from observational data collected during the 2002 International H2O Project (IHOP_2002). The results indicate that rcmin is highly variable both site to site and over diurnal and longer time scales. The mean value at the grassland sites in this study is 96 s m−1 while the mean value for the cropland (winter wheat) sites is one-fourth that value at 24 s m−1. The mean rcmin for all the sites is 72 s m−1 with a standard deviation of 39 s m−1. This variability is due to both the empirical nature of the Jarvis scheme and a combination of changing environmental conditions, such as plant physiology and plant species composition, that are not explicitly considered by the scheme. This variability in rcmin has important implications for land surface modeling where rcmin is often parameterized as a constant. For example, the Noah land surface model parameterizes rcmin for the grasslands and croplands types in this study as 40 s m−1. Tests with the coupled Weather Research and Forecasting (WRF)–Noah model indicate that the using the modified values of rcmin from this study improves the estimates of latent heat flux; the difference between the observed and modeled moisture flux decreased by 50% or more. While land surface models that estimate transpiration using Jarvis-type relationships may be improved by revising the rcmin values for grasslands and croplands, updating the rcmin will not fully account for the variability in rcmin observed in this study. As such, it may be necessary to replace the Jarvis scheme currently used in many land surface and numerical weather prediction models with a physiologically based estimate of the canopy resistance.

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