scholarly journals Assessing the Impacts of Different WRF Precipitation Physics in Hurricane Simulations

2012 ◽  
Vol 27 (4) ◽  
pp. 1003-1016 ◽  
Author(s):  
Nasrin Nasrollahi ◽  
Amir AghaKouchak ◽  
Jialun Li ◽  
Xiaogang Gao ◽  
Kuolin Hsu ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Precipitation Amount ◽  
Cumulus Parameterization ◽  
Microphysics Scheme ◽  
Practical Applications ◽  
Single Moment ◽  
Hurricane Track ◽  
Cumulus Schemes

Abstract Numerical weather prediction models play a major role in weather forecasting, especially in cases of extreme events. The Weather Research and Forecasting Model (WRF), among others, is extensively used for both research and practical applications. Previous studies have highlighted the sensitivity of this model to microphysics and cumulus schemes. This study investigated the performance of the WRF in forecasting precipitation, hurricane track, and landfall time using various microphysics and cumulus schemes. A total of 20 combinations of microphysics and cumulus schemes were used, and the model outputs were validated against ground-based observations. While the choice of microphysics and cumulus schemes can significantly impact model output, it is not the case that any single combination can be considered “ideal” for modeling all characteristics of a hurricane, including precipitation amount, areal extent, hurricane track, and the time of landfall. For example, the model’s ability to simulate precipitation (with the least total bias) is best achieved using Betts–Miller–Janjić (BMJ) cumulus parameterization in combination with the WRF single-moment five-class microphysics scheme (WSM5). It was determined that the WSM5–BMJ, WSM3 (the three-class version of the WSM scheme)–BMJ, and Ferrier microphysics in combination with the Grell–Devenyi cumulus scheme were the best combinations for simulation of the landfall time. However, the hurricane track was best estimated using the Lin et al. and Kessler microphysics options with BMJ cumulus parameterization. Contrary to previous studies, these results indicated that the use of cumulus schemes improves model outputs when the grid size is smaller than 10 km. However, it was found that many of the differences between parameterization schemes may be well within the uncertainty of the measurements.

scholarly journals Fengyun Meteorological Satellite Products for Earth System Science Applications

2021 ◽  
Author(s):  
Di Xian ◽  
Peng Zhang ◽  
Ling Gao ◽  
Ruijing Sun ◽  
Haizhen Zhang ◽  
...  
Keyword(s):  
Data Sharing ◽  
Satellite Data ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Prediction ◽  
Vegetation Indices ◽  
Open Data ◽  
Earth System ◽  
Inversion Algorithm

AbstractFollowing the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.

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 Ensemble Hail Prediction for the Storms of 10 May 2010 in South-Central Oklahoma Using Single- and Double-Moment Microphysical Schemes

2017 ◽  
Vol 145 (12) ◽  
pp. 4911-4936 ◽  
Author(s):  
Jonathan Labriola ◽  
Nathan Snook ◽  
Youngsun Jung ◽  
Bryan Putnam ◽  
Ming Xue
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
Ensemble Forecasts ◽  
South Central ◽  
Single Moment ◽  
Double Moment ◽  
Horizontal Grid ◽  
Severe Weather Event ◽  
Numerical Weather Prediction Models

Explicit prediction of hail using numerical weather prediction models remains a significant challenge; microphysical uncertainties and errors are a significant contributor to this challenge. This study assesses the ability of storm-scale ensemble forecasts using single-moment Lin or double-moment Milbrandt and Yau microphysical schemes in predicting hail during a severe weather event over south-central Oklahoma on 10 May 2010. Radar and surface observations are assimilated using an ensemble Kalman filter (EnKF) at 5-min intervals. Three sets of ensemble forecasts, launched at 15-min intervals, are then produced from EnKF analyses at times ranging from 30 min prior to the first observed hail to the time of the first observed hail. Forty ensemble members are run at 500-m horizontal grid spacing in both EnKF assimilation cycles and subsequent forecasts. Hail forecasts are verified using radar-derived products including information from single- and dual-polarization radar data: maximum estimated size of hail (MESH), hydrometeor classification algorithm (HCA) output, and hail size discrimination algorithm (HSDA) output. Resulting hail forecasts show at most marginal skill, with the level of skill dependent on the forecast initialization time and microphysical scheme used. Forecasts using the double-moment scheme predict many small hailstones aloft, while the single-moment members predict larger hailstones. Near the surface, double-moment members predict larger hailstone sizes than their single-member counterparts. Hail in the forecasts is found to melt too quickly near the surface for members using either of the microphysics schemes examined. Analysis of microphysical budgets in both schemes indicates that both schemes suboptimally represent hail processes, adversely impacting the skill of surface hail forecasts.

scholarly journals Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems

2018 ◽  
Vol 11 (1) ◽  
pp. 147 ◽  
Author(s):  
Byung-Ki Jeon ◽  
Eui-Jong Kim ◽  
Younggy Shin ◽  
Kyoung-Ho Lee
Keyword(s):  
Predictive Control ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Reference Model ◽  
Weather Prediction ◽  
Building Energy ◽  
Energy Model ◽  
Weather Data ◽  
Rc Building ◽  
Mean Square Errors

The aim of this study is to develop a model that can accurately calculate building loads and demand for predictive control. Thus, the building energy model needs to be combined with weather prediction models operated by a model predictive controller to forecast indoor temperatures for specified rates of supplied energy. In this study, a resistance–capacitance (RC) building model is proposed where the parameters of the models are determined by learning. Particle swarm optimization is used as a learning scheme to search for the optimal parameters. Weather prediction models are proposed that use a limited amount of forecasting information fed by local meteorological centers. Assuming that weather forecasting was perfect, hourly outdoor temperatures were accurately predicted; meanwhile, differences were observed in the predicted solar irradiances values. In investigations to verify the proposed method, a seven-resistance, five-capacitance (7R5C) model was tested against a reference model in EnergyPlus using the predicted weather data. The root-mean-square errors of the 7R5C model in the prediction of indoor temperatures on all the specified days were within 0.5 °C when learning was performed using reference data obtained from the previous five days and weather prediction was included. This level of deviation in predictive control is acceptable considering the magnitudes of the loads and demand of the tested building.

Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models: Introduction to the JAS Special Collection

2007 ◽  
Vol 64 (11) ◽  
pp. 3737-3741 ◽  
Author(s):  
Ronald M. Errico ◽  
George Ohring ◽  
Fuzhong Weng ◽  
Peter Bauer ◽  
Brad Ferrier ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Forecasting ◽  
International Workshop ◽  
Weather Prediction ◽  
Satellite Observations ◽  
Atmospheric Sciences ◽  
Numerical Weather ◽  
Special Collection ◽  
Numerical Weather Prediction Models

Abstract To date, the assimilation of satellite measurements in numerical weather prediction (NWP) models has focused on the clear atmosphere. But satellite observations in the visible, infrared, and microwave provide a great deal of information on clouds and precipitation. This special collection describes how to use this information to initialize clouds and precipitation in models. Since clouds and precipitation often occur in sensitive regions for forecast impacts, such improvements are likely necessary for continuing to acquire significant gains in weather forecasting. This special collection of the Journal of the Atmospheric Sciences is devoted to articles based on papers presented at the International Workshop on Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models, in Lansdowne, Virginia, in May 2005. This introduction summarizes the findings of the workshop. The special collection includes review articles on satellite observations of clouds and precipitation (Stephens and Kummerow), parameterizations of clouds and precipitation in NWP models (Lopez), radiative transfer in cloudy/precipitating atmospheres (Weng), and assimilation of cloud and precipitation observations (Errico et al.), as well as research papers on these topics.

scholarly journals Variance-Based Sensitivity Analysis: Preliminary Results in COAMPS

2014 ◽  
Vol 142 (5) ◽  
pp. 2028-2042 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe ◽  
James D. Doyle ◽  
Nicholas C. Lederer
Keyword(s):  
Sensitivity Analysis ◽  
Prediction Models ◽  
Theoretical Calculations ◽  
Weather Prediction ◽  
Surface Fluxes ◽  
Cumulus Parameterization ◽  
Convective Precipitation ◽  
Primary Concern ◽  
Total Precipitation ◽  
Parameter Values

Abstract Numerical weather prediction models have a number of parameters whose values are either estimated from empirical data or theoretical calculations. These values are usually then optimized according to some criterion (e.g., minimizing a cost function) in order to obtain superior prediction. To that end, it is useful to know which parameters have an effect on a given forecast quantity, and which do not. Here the authors demonstrate a variance-based sensitivity analysis involving 11 parameters in the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). Several forecast quantities are examined: 24-h accumulated 1) convective precipitation, 2) stable precipitation, 3) total precipitation, and 4) snow. The analysis is based on 36 days of 24-h forecasts between 1 January and 4 July 2009. Regarding convective precipitation, not surprisingly, the most influential parameter is found to be the fraction of available precipitation in the Kain–Fritsch cumulus parameterization fed back to the grid scale. Stable and total precipitation are most affected by a linear factor that multiplies the surface fluxes; and the parameter that most affects accumulated snow is the microphysics slope intercept parameter for snow. Furthermore, all of the interactions between the parameters are found to be either exceedingly small or have too much variability (across days and/or parameter values) to be of primary concern.

Atmospheric angular momentum forecasts as novel tests of global numerical weather prediction models

1991 ◽  
Vol 334 (1633) ◽  
pp. 55-92 ◽  
Keyword(s):  
Angular Momentum ◽  
Numerical Weather Prediction ◽  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Angular Momentum ◽  
Axial Component ◽  
Practical Applications ◽  
Numerical Weather ◽  
Three Components

The global circulation of the terrestrial atmosphere exhibits fluctuations of considerable amplitude in all three components of its total angular momentum on interannual, seasonal and shorter timescales. The fluctuations must be intimately linked with nonlinear barotropic and baroclinic energetic conversion processes throughout the whole atmosphere and it is advocated that studies of routinely produced determinations of atmospheric angular momentum (AAM) changes be incorporated into systematic diagnostic investigations of large-scale atmospheric flows, AAM fluctuations are generated by dynamical interactions between the atmosphere and the underlying planet. These excite tiny but measurable compensating fluctuations in the rotation vector of the massive solid Earth, thereby ensuring conservation of the angular momentum of the whole system. Forecasts and analyses of changes in AAM from the output of a global numerical weather prediction (GNWP) model constitute a stringent test of the model. Successful forecasts of the axial com ponent of AAM, and hence of irregular non-tidal components of short-term changes in the Earth’s rotation, would find practical applications in various areas of astronomy and geodesy, such as spacecraft navigation. Reported in this paper are the main findings of intercomparisons of analyses and forecasts of changes in all three components of AAM obtained from the operational GNWP models at the United Kingdom Meteorological Office (UKMO) and the European Centre for Medium Range Weather Forecasts (ECMWF), over the period covering the two years 1987 and 1988. Included in the results obtained is the finding that useful forecasts of changes in the axial component of AAM can be made out to 5 days and even slightly longer.

Improvements to melting snow behavior in an NWP bulk microphysics scheme

2020 ◽  
Author(s):  
Emilie C. Iversen ◽  
Gregory Thompson ◽  
Bjørn Egil Nygaard
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Bimodal Distribution ◽  
Fall Velocity ◽  
Microphysics Scheme ◽  
Melting Snow ◽  
Melting Layer ◽  
Computational Performance ◽  
Fall Speed

<p>Snow falling into a melting layer will eventually consist of a fraction of meltwater and hence change its characteristics in terms of size, shape, density, fall speed and stickiness. Given that these characteristics contribute to determine the phase and amount of precipitation reaching the ground, precisely predicting such are important in order to obtain accurate weather forecasts for which society depends on. For example, in hydrological modelling precipitation phase at the surface is a first-order driver of hydrological processes in a water shed. Also, melting snow exerts a possible threat to critical infrastructure because the wet, sticky snow may adhere to the structures and form heavy ice sleeves.</p><p>Most widely used bulk microphysical parameterization schemes part of numerical weather prediction models represent only purely solid or liquid hydrometeors, and so melting particle characteristics are either ignored or represented by parent species with simple conditions for behavior in the melting layer. The Thompson microphysics scheme is explicitly developed for forecasting winter conditions in real-time as part of the WRF model, and to maintain computational performance, the introduction of additional prognostic variables is undesirable. This research aims at improving the Thompson scheme with respect to melting snow characteristics using a physically based approximation for the snowflake melted fraction, as well as a new definition of melting level and melting particle fall velocity. A real 3D WRF case is set up to compare with in-situ measurements of hydrometeor size and fall velocity from a disdrometer and a vertically pointing Doppler radar deployed during the Olympic Mountain Experiment (OLYMPEX). The modified microphysics scheme is able to replicate the bimodal distribution of fall speed – diameter relations typical of mixed precipitation seen in disdrometer data, as well as the non-linear increase in snow fall speed with melted fraction through the melting layer.</p>

scholarly journals The ability of the ICE-T microphysics scheme in HARMONIE-AROME to predict aircraft icing

2021 ◽  
Author(s):  
Tim Carlsen ◽  
Morten Køltzow ◽  
Trude Storelvmo
Keyword(s):  
Water Content ◽  
Numerical Weather Prediction ◽  
Liquid Water ◽  
Prediction Models ◽  
Weather Prediction ◽  
Supercooled Liquid ◽  
Liquid Water Content ◽  
Aircraft Icing ◽  
Microphysics Scheme ◽  
Numerical Weather

Abstract In-cloud icing is a major hazard for aviation traffic and forecasting of these events is an important task for weather agencies worldwide. A common tool utilised by aviation forecasters is an icing intensity index based on supercooled liquid water from numerical weather prediction models. We seek to validate the modified microphysics scheme, ICE-T, in the HARMONIE-AROME numerical weather prediction model with respect to aircraft icing. Icing intensities and supercooled liquid water derived from two 3-month winter season simulations with the original microphysics code, CTRL, and ICE-T are compared with pilot reports of icing and satellite retrieved values of liquid and ice water content from CloudSat-CALIPSO and liquid water path from AMSR-2. The results show increased supercooled liquid water and higher icing indices in ICE-T. Several different thresholds and sizes of neighbourhood areas for icing forecasts were tested out, and ICE-T captures more of the reported icing events for all thresholds and nearly all neighbourhood areas. With a higher frequency of forecasted icing, a higher false-alarm ratio cannot be ruled out, but is not possible to quantify due to the lack of no-icing observations. The increased liquid water content in ICE-T shows a better match with the retrieved satellite observations, yet the values are still greatly underestimated at lower levels. Future studies should investigate this issue further, as liquid water content also has implications for downstream processes such as the cloud radiative effect, latent heat release, and precipitation.

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