Application of the LEPS technique for Quantitative Precipitation Forecasting (QPF) in Southern Italy: a preliminary study (original) (raw)
The application of LEPS technique for Quantitative Precipitation Forecast (QPF) in Southern Italy
Advances in Geosciences, 2006
This paper reports preliminary results of a Limited area model Ensemble Prediction System (LEPS), based on RAMS, for eight case studies of moderate-intense precipitation over Calabria, the southernmost tip of the Italian peninsula. LEPS aims to transfer the benefits of a probabilistic forecast from global to regional scales in countries where local orographic forcing is a key factor to force convection.
Atmospheric Research, 2009
The NOAA National Weather Service has maintained an automated, centralized 0-3 h prediction system for probabilistic quantitative precipitation forecasts since 2001. This advective-statistical system (ADSTAT) produces probabilities that rainfall will exceed multiple threshold values up to 50 mm at some location within a 40-km grid box. Operational characteristics and development methods for the system are described. Although development data were stratified by season and time of day, ADSTAT utilizes only a single set of nation-wide equations that relate predictor variables derived from radar reflectivity, lightning, satellite infrared temperatures, and numerical prediction model output to rainfall occurrence. A verification study documented herein showed that the operational ADSTAT reliably models regional variations in the relative frequency of heavy rain events. This was true even in the western United States, where no regional-scale, gridded hourly precipitation data were available during the development period in the 1990s. An effort was recently launched to improve the quality of ADSTAT forecasts by regionalizing the prediction equations and to adapt the model for application in the Czech Republic. We have experimented with incorporating various levels of regional specificity in the probability equations. The geographic localization study showed that in the warm season, regional climate differences and variations in the diurnal temperature cycle have a marked effect on the predictor-predictand relationships, and thus regionalization would lead to better statistical reliability in the forecasts.
Probabilistic high-resolution forecast of heavy precipitation over Central Europe
Natural Hazards and Earth System Sciences, 2004
The limited-area ensemble prediction system COSMO-LEPS has been running operationally at ECMWF since November 2002. Five runs of the non-hydrostatic limited-area model Lokal Modell (LM) are available every day, nested on five selected members of three consecutive 12-h lagged ECMWF global ensembles. The limited-area ensemble forecasts range up to 120 h and LM-based probabilistic products are disseminated to several national weather services. COSMO-LEPS has been constructed in order to have a probabilistic system with high resolution, focussing the attention on extreme events in regions with complex orography. In this paper, the performance of COSMO-LEPS for a heavy precipitation event that affected Central Europe in August 2002 has been examined. At the 4-day forecast range, the probability maps indicate the possibility of the overcoming of high precipitation thresholds (up to 150 mm/24 h) over the region actually affected by the flood. Furthermore, one out of the five ensemble members predicts 4 days ahead a precipitation structure very similar to the observed one.
Meteorologische Zeitschrift, 2008
In the year 2007 a General Observation Period (GOP) has been performed within the German Priority Program on Quantitative Precipitation Forecasting (PQP). By optimizing the use of existing instrumentation a large data set of in-situ and remote sensing instruments with special focus on water cycle variables was gathered over the full year cycle. The area of interest covered central Europe with increasing focus towards the Black Forest where the Convective and Orographically-induced Precipitation Study (COPS) took place from June to August 2007. Thus the GOP includes a variety of precipitation systems in order to relate the COPS results to a larger spatial scale. For a timely use of the data, forecasts of the numerical weather prediction models COSMO-EU and COSMO-DE of the German Meteorological Service were tailored to match the observations and perform model evaluation in a near real-time environment. The ultimate goal is to identify and distinguish between different kinds of model deficits and to improve process understanding.
Bulletin of the American Meteorological Society, 1998
Quantitative precipitation forecasting (QPF) is the most important and significant challenge of weather forecasting. Advances in computing and observational technology combined with theoretical advances regarding the chaotic nature of the atmosphere offer the possibility of significant improvement in QPF. To achieve these improvements, this report recommends research focusing on 1) improving the accuracy and temporal and spatial resolution of the rainfall observing system; 2) performing process and climatological studies using the modernized observing system; 3) designing new data-gathering strategies for numerical model initialization; and 4) defining a probabilistic framework for precipitation forecasting and verification. Advances on the QPF problem will require development of advanced ensemble techniques that account for forecast uncertainty, stemming from sampling error and differences in model physics and numerics and development of statistical techniques for using observational data to verify probabilistic QPF in a way that is consistent with the chaotic nature of the precipitation process.
Statistical properties and validation of Quantitative Precipitation Forecast
2010
Observed precipitation fields show a high variability both in space and time and the amount of rainfall could vary a lot within a short distance.(Zepeda-Arce et al.,2000). The increasing of horizontal resolution in NWP models seems to enable them to reproduce this variability, even if frequent errors in time and space positioning make difficult a grid-point based employment of models QPF. In order to asses the ability of the models in reproducing the variability of the precipitation fields, we investigated the statistical properties of the observed and forecasted rain values falling within a predefined geographical area and in a specific time period (also called boxes). In particular we studied the distribution function (pdf )and evaluated some summarizing quantities, such as the mean, the maximum value and quantiles for each of the selected box. Results for different size of the chosen areas and period of time are used to validate the QPF of the COSMO suites that run operationally ...
Weather and Forecasting, 2017
A neighborhood postprocessing approach that relates quantitative precipitation forecasts (QPF) to probability of precipitation (PoP) forecasts applied to a single model run was found by Schaffer et al. to be as good as traditional ensemble-based approaches using 10 members in 30-h forecasts of convective precipitation. The present study evaluates if PoP forecasts derived from additional variations of the approach can improve PoP forecasts further compared with previous methods. Ensemble forecasts from the Center for Analysis and Prediction of Storms (CAPS) are used for neighborhood tests comparing a single model run and a traditional ensemble. In the first test, PoP forecasts for different combinations of training and testing datasets using a single model member with 4-km grid spacing are compared against those obtained with a 10-member traditional ensemble. Overall, forecasts for the neighborhood approach with just one member are only slightly less accurate to those using a more tr...
2007
This note examines the connection between the probability of precipitation and forecasted amounts from the NCEP Eta (now known as the North American Mesoscale model) and Aviation (AVN; now known as the Global Forecast System) models run over a 2-yr period on a contiguous U.S. domain. Specifically, the quantitative precipitation forecast (QPF)-probability relationship found recently by Gallus and Segal in 10-km grid spacing model runs for 20 warm season mesoscale convective systems is tested over this much larger temporal and spatial dataset. A 1-yr period was used to investigate the QPF-probability relationship, and the predictive capability of this relationship was then tested on an independent 1-yr sample of data. The same relationship of a substantial increase in the likelihood of observed rainfall exceeding a specified threshold in areas where model runs forecasted higher rainfall amounts is found to hold over all seasons. Rainfall is less likely to occur in those areas where the models indicate none than it is elsewhere in the domain; it is more likely to occur in those regions where rainfall is predicted, especially where the predicted rainfall amounts are largest. The probability of rainfall forecasts based on this relationship are found to possess skill as measured by relative operating characteristic curves, reliability diagrams, and Brier skill scores. Skillful forecasts from the technique exist throughout the 48-h periods for which Eta and AVN output were available. The results suggest that this forecasting tool might assist forecasters throughout the year in a wide variety of weather events and not only in areas of difficult-to-forecast convective systems.
Weather and Forecasting, 2005
This paper presents the first systematic limited area model (LAM) precipitation verification work over Italy. A resampling technique was used to provide skill score results along with confidence intervals. Two years of data were used, starting in October 2000. Two operational LAMs have been considered, the Limited Area Model Bologna (LAMBO) operating at the Agenzia Regionale Prevenzione e Ambiente-Servizio Meteorologico Regionale (ARPA-SMR) of the Emilia-Romagna region, and the QUADRICS Bologna Limited Area Model (QBOLAM) running at the Agenzia per la Protezione dell'Ambiente e per i Servizi Tecnici (APAT). A 24-h forecast skill score comparison was first performed on the native 0.1°h igh-resolution grids, using a Barnes scheme to produce the observed 24-h accumulated rainfall analysis. Two nonparametric skill scores were used: the equitable threat score (ETS) and the Hanssen and Kuipers score (HK). Frequency biases (BIA) were also calculated. LAM forecasts were also remapped on a lowerresolution grid (0.5°), using a nearest-neighbor average method; this remapping allowed for comparison with ECMWF model forecasts, and for LAM intercomparisons at lower resolution, with the advantage of reducing the skill score sensitivity to small displacements errors. LAM skill scores depend on the resolution of the verification grid, with an increase when they are verified on a lower-resolution grid. The selected LAMs have a higher BIA compared to ECMWF, showing a tendency to overforecast precipitation, especially along mountain ranges, possibly due to undesired effects from the large-scale and/or convective precipitation parameterizations. Lower ECMWF BIA accounts for skill score differences. LAMBO precipitation forecasts during winter (adjusted for BIA differences) have less misses than ECMWF over the islands of Sardinia and Sicily. Higher-resolution orography definitely adds value to LAM forecasts.
A spatial verification method applied to the evaluation of high-resolution ensemble forecasts
The verification of ensemble systems is being operationally carried out in several meteorological centres. However, the main operational ensemble systems have a coarser spatial resolution with respect to the deterministic runs. Only recently, high-resolution limited-area ensembles have started to be run on a regular basis. Their verification requires combining the usual probabilistic evaluation with the statistical verification techniques which are being developed for high-resolution model forecasts (1–10 km). These techniques permit to evaluate a deterministic forecast in a probabilistic manner, by taking into account the spatio-temporal distribution of the forecast at different scales. In this work, a spatial verification technique, called 'distributional method', is used to verify the Consortium for Small-scale MOdeling Limited-area Ensemble Prediction System (COSMO–LEPS) ensemble system, a mesoscale ensemble with 10 km horizontal resolution. The system is mainly designed to give probabilistic assistance in the forecast of severe weather, in particular of intense precipitation possibly leading to floods, hence verification is focused on the ability of the system in forecasting precipitation at high spatial resolution. The methodology is based on a comparison of forecasts and observations in terms of some parameters of their distributions, evaluated after the values are aggregated over boxes of selected size. In particular, performances in terms of average, a few percentiles and maximum forecast value in a box are considered. The system is compared against European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF EPS), addressing the issues of an intercomparison between a higher-resolution smaller-size ensemble and a lower-resolution larger-size one. Results show that when the forecast of the average amount of precipitation over an area is concerned, COSMO–LEPS is more skilful than the Ensemble Prediction System (EPS) only from the resolution point of view. Therefore, although not properly calibrated, it is more capable of distinguishing between events and non-events, especially for moderate and high precipitation. Furthermore, COSMO–LEPS has skill in forecasting the occurrence of precipitation peaks over an area, irrespective of the exact location. The analysis of the score behaviour as a function of the distribution parameter shows that EPS has the maximum skill in reproducing the central part of the observed precipitation distribution over an area of about 10 000 km 2 , while COSMO–LEPS is more skilful in reproducing the tail of the observed precipitation distribution. The problem of the predictability of precipitation at different spatial scales is also investigated, showing the role of different system resolutions.