Operational Numerical Weather Prediction Models Research Articles

On the Impact of NWP Model Background on Very High–Resolution Analyses in Complex Terrain

Abstract The capability to accurately analyze the spatial distribution of temperature and wind at very high spatial (2.5–1 km) and temporal (60–5 min) resolutions is of interest in many modern techniques (e.g., nowcasting and statistical downscaling). In addition to observational data, the generation of such analyses requires background information to adequately resolve nonstatic, small-scale phenomena. Numerical weather prediction (NWP) models are of continuously increasing skill and are more capable of providing valuable information on convection-resolving scales. The present paper discusses the impact of two operational NWP models on hourly 2-m temperature and 10-m wind analyses as created by the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which includes a topographic downscaling procedure. The NWP models used for this study are a revised version of ARPEGE–ALADIN (ALARO; 4.8-km resolution) and the Applications of Research to Operations at Mesoscale (AROME; 2.5-km resolution). Based on a case study and a longer-term validation, it is shown that, generally, the finer the grid spacing of the background model and the higher the resolution of the target grid in the downscaling procedure, the slightly more accurate is the analysis. This is especially true for wind analyses in mountainous regions, where a realistic simulation of topographic effects is crucial. In the case of 2-m temperature, the impact is less pronounced, but the topographic downscaling at very high resolution at least adds detail in complex terrain. However, in the vicinity of station observations, the analysis algorithm is capable of spatially adjusting the larger biases found in the ALARO model while having a lesser effect on the downscaled AROME model.

Quarterly numerical weather prediction model performance summary - April to June 2015

Quarterly numerical weather prediction model performance summary - April to June 2015

Quarterly numerical weather prediction model performance summary - January to March 2015

Quarterly numerical weather prediction model performance summary - January to March 2015

Examining the Roles of the Easterly Wave Critical Layer and Vorticity Accretion during the Tropical Cyclogenesis of Hurricane Sandy*

Abstract The tropical cyclogenesis sequence of Hurricane Sandy is examined. It is shown that genesis occurs within a recirculating Kelvin cat’s-eye flow of a westward-propagating tropical wave. The cat’s-eye flow is able to provide a protective environment for the mesoscale vortex to grow and is characterized by gradual column moistening and increased areal coverage of deep cumulus convection. These findings are generally consistent with a recently proposed tropical cyclogenesis sequence referred to as the “marsupial paradigm.” Sandy’s cyclogenesis provides a useful illustration of the marsupial paradigm within a partially open recirculating region, with the opening located south of the pouch center. It is suggested that the opening acts to enhance the genesis process because it is adjacent to an environment characterized by warm, moist air, conditions favorable for tropical cyclogenesis. From a dynamical perspective, accretion of low-level cyclonic vorticity filaments into the developing vortex from several sources (the South American convergence zone, an easterly wave located west of the pre-Sandy wave, and cyclonic vorticity generated along Hispaniola) is documented. Organization and growth of the nascent storm is enhanced by this accretion of cyclonic vorticity. A Lagrangian trajectory analysis is used to assess potential contributions to Sandy’s spinup from a Caribbean gyre and the easterly wave that formed Hurricane Tony. This analysis indicates that these features are outside of the Lagrangian flow boundaries that define the pre-Sandy wave and do not directly contribute to spinup of the vortex. Finally, the effectiveness of forecasts from the U.S. and European operational numerical weather prediction models is discussed for this case.

Assessment of small-scale integrated water vapour variability during HOPE

Abstract. The spatio-temporal variability of integrated water vapour (IWV) on small scales of less than 10 km and hours is assessed with data from the 2 months of the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE). The statistical intercomparison of the unique set of observations during HOPE (microwave radiometer (MWR), Global Positioning System (GPS), sun photometer, radiosondes, Raman lidar, infrared and near-infrared Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellites Aqua and Terra) measuring close together reveals a good agreement in terms of random differences (standard deviation ≤1 kg m−2) and correlation coefficient (≥ 0.98). The exception is MODIS, which appears to suffer from insufficient cloud filtering. For a case study during HOPE featuring a typical boundary layer development, the IWV variability in time and space on scales of less than 10 km and less than 1 h is investigated in detail. For this purpose, the measurements are complemented by simulations with the novel ICOsahedral Nonhydrostatic modelling framework (ICON), which for this study has a horizontal resolution of 156 m. These runs show that differences in space of 3–4 km or time of 10–15 min induce IWV variabilities on the order of 0.4 kg m−2. This model finding is confirmed by observed time series from two MWRs approximately 3 km apart with a comparable temporal resolution of a few seconds. Standard deviations of IWV derived from MWR measurements reveal a high variability (> 1 kg m−2) even at very short time scales of a few minutes. These cannot be captured by the temporally lower-resolved instruments and by operational numerical weather prediction models such as COSMO-DE (an application of the Consortium for Small-scale Modelling covering Germany) of Deutscher Wetterdienst, which is included in the comparison. However, for time scales larger than 1 h, a sampling resolution of 15 min is sufficient to capture the mean standard deviation of IWV. The present study shows that instrument sampling plays a major role when climatological information, in particular the mean diurnal cycle of IWV, is determined.

Quarterly numerical weather prediction model performance summary - October to December 2014

Quarterly numerical weather prediction model performance summary - October to December 2014

Predicting Small-Scale, Short-Lived Downbursts: Case Study with the NWP Limited-Area ALARO Model for the Pukkelpop Thunderstorm

Abstract The authors consider a thunderstorm event in 2011 during a music festival in Belgium that produced a short-lived downburst of a diameter of less than 100 m. This is far too small to be resolved by the kilometric resolutions of today’s operational numerical weather prediction models. Operational forecast models will not run at hectometric resolutions in the foreseeable future. The storm caused five casualties and raised strong societal questions regarding the predictability of such a traumatic weather event. In this paper it is investigated whether the downdrafts of a parameterization scheme of deep convection can be used as proxies for the unresolved downbursts. To this end the operational model ALARO [a version of the Action de Recherche Petite Echelle Grande Echelle-Aire Limitée Adaptation Dynamique Développement International (ARPEGE-ALADIN) operational limited area model with a revised and modular structure of the physical parameterizations] of the Royal Meteorological Institute of Belgium is used. While the model in its operational configuration at the time of the event did not give a clear hint of a downburst event, it has been found that (i) the use of unsaturated downdrafts and (ii) some adaptations of the features of this downdraft parameterization scheme, specifically the sensitivity to the entrainment and friction, can make the downdrafts sensitive enough to the surrounding resolved-scale conditions to make them useful as indicators of the possibility of such downbursts.

Downscaling wind energy resource from mesoscale to microscale model and data assimilating field measurements

The main objective of this research work is to develop and evaluate several coupling methods between operational Numerical Weather Prediction (NWP) model and Computational Fluid Dynamics (CFD) model and data assimilate the field measurements into the CFD model. To address the problem of high spatial variation of the topography on the domain lateral boundaries between NWP and CFD domain boundaries, 3 methods – translation, extrapolation and Cressman interpolation are used to impose the NWP model data on the CFD domain lateral boundaries. Newtonian relaxation data assimilation technique is used to incorporate the field measurement data into the CFD simulations. These techniques are studied in a complex site located in southern France. Comparison of wind profiles between the CFD simulation, measurements and CFD simulation with data assimilation are discussed. This combination of state-of-the-art techniques in NWP, CFD, and field data assimilation will provide the basis of a more accurate wind resource assessment method.

Assimilation of lake water surface temperature observations using an extended Kalman filter

A new extended Kalman filter (EKF)-based algorithm to assimilate lake water surface temperature (LWST) observations into the lake model/parameterisation scheme Freshwater Lake (FLake) has been developed. The data assimilation algorithm has been implemented into the stand-alone offline version of FLake. The mixed and non-mixed regimes in lakes are treated separately by the EKF algorithm. The timing of the ice period is indicated implicitly: no ice if water surface temperature is measured. Numerical experiments are performed using operational in-situ observations for 27 lakes and merged observations (in-situ plus satellite) for 4 lakes in Finland. Experiments are analysed, potential problems are discussed, and the role of early spring observations is studied. In general, results of experiments are promising: (1) the impact of observations (calculated as the normalised reduction of the LWST root mean square error comparing to the free model run) is more than 90% and (2) in cross-validation (when observations are partly assimilated, partly used for validation) the normalised reduction of the LWST error standard deviation is more than 65%. The new data assimilation algorithm will allow prognostic variables in the lake parameterisation scheme to be initialised in operational numerical weather prediction models and the effects of model errors to be corrected by using LWST observations.

Quarterly numerical weather prediction model performance summary - July to September 2014

Quarterly numerical weather prediction model performance summary - July to September 2014

Impact of satellite-based lake surface observations on the initial state of HIRLAM. Part II: Analysis of lake surface temperature and ice cover

This paper presents results from a study on the impact of remote-sensing Lake Surface Water Temperature (LSWT) observations in the analysis of lake surface state of a numerical weather prediction (NWP) model. Data assimilation experiments were performed with the High Resolution Limited Area Model (HIRLAM), a three-dimensional operational NWP model. Selected thermal remote-sensing LSWT observations provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Along-Track Scanning Radiometer (AATSR) sensors onboard the Terra/Aqua and ENVISAT satellites, respectively, were included into the assimilation. The domain of our experiments, which focussed on two winters (2010–2011 and 2011–2012), covered northern Europe. Validation of the resulting objective analyses against independent observations demonstrated that the description of the lake surface state can be improved by the introduction of space-borne LSWT observations, compared to the result of pure prognostic parameterisations or assimilation of the available limited number of in-situ lake temperature observations. Further development of the data assimilation methods and solving of several practical issues are necessary in order to fully benefit from the space-borne observations of lake surface state for the improvement of the operational weather forecast. This paper is the second part of a series of two papers aimed at improving the objective analysis of lake temperature and ice conditions in HIRLAM.

The sensitivity of simulated high clouds to ice crystal fall speed, shape and size distribution

The sensitivity of an operational numerical weather prediction model to the parametrized microphysical properties of ice hydrometeors is examined. The effects of varying ice‐particle size distribution, fall speed, mass and depositional capacitance are considered in kilometre‐scale simulations of midlatitude cloud systems and 20 year global climate integrations. It is shown that the observed sensitivity can be obtained from steady‐state arguments, whereby the vertical moisture flux is balanced by the hydrometeor sedimentation flux and supersaturation production is in equilibrium with depositional growth. The high‐resolution simulations are compared with in situ measurements from the Constrain field campaign (Prestwick, UK).

Quarterly Numerical Weather Prediction Model Performance Summary - April to June 2014

Quarterly Numerical Weather Prediction Model Performance Summary - April to June 2014

Statistical properties of precipitation as observed by the TRMM precipitation radar

AbstractThe statistical properties of tropic‐subtropic precipitation are revealed with 13 years of Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) measurements. About 3% of PR observations are raining pixels. The average daily rainfall over 37.5°N–37.5°S is 1.28, 1.18, and 2.46 mm d−1 for convective, stratiform, and total rain, respectively, indicating 51.85% from convective rain and 48.09% from stratiform rain. The related values are 1.300, 1.272, and 2.573 mm d−1 over ocean and 1.22, 0.97, and 2.19 mm d−1 over land, indicating a convective rain fraction of 50.51% over ocean and 55.77% over land. The 92% (93%) and 73% (55%) of rain events over ocean (land) are from stratiform and convective rain <5 mm h−1, respectively, while the associated rainfall contributions in stratiform and convective rain are 62% (68%) and 27% (15%) over ocean (land). Results demonstrate that contributions from large rain intensity events are very importation in total precipitation, especially over land. The rainfall missed by TRMM PR is mostly light rain and does not significantly impact large‐scale statistics of convective and stratiform rain amount. Light rain will increase the total precipitation by about 10% and, if considered a separate category, decrease the observed convective and stratiform rain contributions about 10% over the PR domain. These statistical properties of precipitation could be utilized as a baseline in the assessment of precipitation from operational numerical weather prediction and climate models.

Nowcasting with the AROME Model: First Results from the High-Resolution AROME Airport

Abstract Applications of Research to Operations at Mesoscale (AROME) Airport is the high-resolution version of the French operational numerical weather prediction model AROME. The purpose of AROME Airport is to provide rapidly updated forecasts with a 500-m resolution for nowcasting over an airport and to help with the prediction of wake vortices in order to increase the efficiency of the airport. Here, the model is evaluated for the area around Paris Charles de Gaulle Airport (CDG), France. AROME Airport and other configurations of the AROME model are compared to observations during two 4-week periods, when additional observations are available for the area around CDG (May–June 2011 and September–October 2012). The root-mean-square error and the bias are calculated for the 2-m temperature, 10-m wind speed, and the vertical distribution of the wind speed using the available measurements. The performance of AROME Airport is compared to the operational configuration of the AROME model both for synoptic hours and in a more realistic setting using the forecasts from AROME Airport starting every hour. It is shown that the forecasts from AROME Airport are an improvement with respect to the operational when comparing runs from all forecast hours, particularly for the wind speed, but when comparing the synoptic hours, the results are less clear. The sensitivity of AROME Airport to its data assimilation and initial and lateral boundary conditions is also discussed.

Surface temperature forecast skill comparison for the west coast of Saudi Arabia

Given the growing interest of the general public in accessing commercial weather forecasts through various media outlets and the available impetuses for promoting tourism in Saudi Arabia (SA), a first attempt is made to present a forecast skill comparison for surface temperature in four cities (Wejh, Yenbo, Jeddah, and Gizan) along the west coast of SA, for the 61-day transitional period (from January 16 to March 16) between the December-January-February (DJF) and the March-April-May (MAM) seasons. A simple skill score comparison method is used to assess the next-day city forecasts for surface temperature from six commercial weather forecast providers based on the operational numerical weather prediction (NWP) model outputs. All the NWP model forecast providers performed better than the respective daily climatology (Clm) for each station. Depending upon the station and the provider, the absolute average maximum daily surface temperature difference between the forecasts and the observations was less than 2 °C. Daily surface temperature forecasts from two versions of an atmospheric-ocean general circulation model are also compared to assess their performance for these coastal locations.

Impact of aerosols on the forecast accuracy of solar irradiance calculated by a numerical weather prediction model

The impact of aerosols on the forecast accuracy of solar irradiance calculated by a fine-scale, one day-ahead, and operational numerical weather prediction model (NWP) is investigated in this study. In order to investigate the impact of aerosols only, the clear sky period is chosen, which is defined as when there are no clouds in the observation data and in the forecast data at the same time. The evaluation of the forecast accuracy of the solar irradiance is done at a single observation point that is sometimes affected by aerosol events. The analysis period is one year from April 2010 to March 2011. During the clear sky period, the root mean square errors (RMSE) of the global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI) are 40.0 W m−2, 84.0 Wm−2, and 47.9 W m−2, respectively. During one extreme event, the RMSEs of the GHI, DNI, and DHI are 70.1 W m−2, 211.6 W m−2, and 141.7 W m−2, respectively. It is revealed that the extreme events were caused by aerosols such as dust or haze. In order to investigate the impact of the aerosols, the sensitivity experiments of the aerosol optical depth (AOD) for the extreme events are executed. The best result is obtained by changing the AOD to 2.5 times the original AOD. This changed AOD is consistent with the satellite observation. Thus, it is our conclusion that an accurate aerosol forecast is important for the forecast accuracy of the solar irradiance.

Forecasting quantitative rainfall over India using multi-model ensemble technique

A new approach to ensemble forecasting of rainfall over India based on daily outputs of four operational numerical weather prediction (NWP) models in the medium-range timescale (up to 5 days) is proposed in this study. Four global models, namely ECMWF, JMA, GFS and UKMO available on real-time basis at India Meteorological Department, New Delhi, are used simultaneously with adequate weights to obtain a multi-model ensemble (MME) technique. In this technique, weights for each NWP model at each grid point are assigned on the basis of unbiased mean absolute error between the bias-corrected forecast and observed rainfall time series of 366 daily data of 3 consecutive southwest monsoon periods (JJAS) of 2008, 2009 and 2010. Apart from MME, a simple ensemble mean (ENSM) forecast is also generated and experimented. The prediction skill of MME is examined against observed and corresponding outputs of each constituent model during monsoon 2011. The inter-comparison reveals that MME is able to provide more realistic forecast of rainfall over Indian monsoon region by taking the strength of each constituent model. It has been further found that the weighted MME technique has higher skill in predicting daily rainfall compared to ENSM and individual member models. RMSE is found to be lowest in MME forecasts both in magnitude and area coverage. This indicates that fluctuations of day-to-day errors are relatively less in the MME forecast. The inter-comparison of domain-averaged skill scores for different rainfall thresholds further clearly demonstrates that the MME algorithm improves slightly above the ENSM and member models.

Quarterly Numerical Weather Prediction Model Performance Summary-October 2013 to March 2014

Quarterly Numerical Weather Prediction Model Performance Summary-October 2013 to March 2014

Inducing Tropical Cyclones to Undergo Brownian Motion: A Comparison between Itô and Stratonovich in a Numerical Weather Prediction Model

Abstract Stochastic parameterization has become commonplace in numerical weather prediction (NWP) models used for probabilistic prediction. Here a specific stochastic parameterization will be related to the theory of stochastic differential equations and shown to be affected strongly by the choice of stochastic calculus. From an NWP perspective the focus will be on ameliorating a common trait of the ensemble distributions of tropical cyclone (TC) tracks (or position); namely, that they generally contain a bias and an underestimate of the variance. With this trait in mind the authors present a stochastic track variance inflation parameterization. This parameterization makes use of a properly constructed stochastic advection term that follows a TC and induces its position to undergo Brownian motion. A central characteristic of Brownian motion is that its variance increases with time, which allows for an effective inflation of an ensemble’s TC track variance. Using this stochastic parameterization the authors present a comparison of the behavior of TCs from the perspective of the stochastic calculi of Itô and Stratonovich within an operational NWP model. The central difference between these two perspectives as pertains to TCs is shown to be properly predicted by the stochastic calculus and the Itô correction. In the cases presented here these differences will manifest as overly intense TCs, which, depending on the strength of the forcing, could lead to problems with numerical stability and physical realism.