Verification Of Precipitation Research Articles

Performance of the Canadian Arctic Prediction System during the YOPP Special Observing Periods

ABSTRACT As a contribution to the Year of Polar Prediction (YOPP), Environment and Climate Change Canada (ECCC) developed the Canadian Arctic Prediction System (CAPS), a high-resolution (3-km horizontal grid-spacing) deterministic Numerical Weather Prediction (NWP) system that ran in real-time from February 2018 to November 2021. During YOPP, ECCC was also running two other operational systems that cover the Arctic: the 10-km Regional Deterministic Prediction System (RDPS) and the 25-km Global Deterministic Prediction System (GDPS). The performance of these three systems over the Arctic was monitored and routinely compared during 2018, both subjectively and with objective verification scores. This work provides a description of CAPS and compares the surface variable objective verification for the Canadian deterministic NWP systems operational during YOPP, focusing on the Arctic winter and summer Special Observing Periods (Feb-March and July-Aug-Sept, 2018). CAPS outperforms RDPS and GDPS in predicting near-surface temperature, dew-point temperature, wind and precipitation, in both seasons and domains. All three systems exhibit a diurnal cycle in the near-surface temperature biases, with maxima at night and minima in day-time. In order to mitigate representativeness issues associated with complex topography, model tile temperatures are adjusted to the station elevation by applying a standard atmosphere lapse-rate: especially for the coarse-resolution models, the lapse-rate adjustment reduces the temperature cold biases characterising mountain terrains. Verification of winter precipitation is performed by adjusting solid precipitation measurement errors from the undercatch in windy conditions: the Canadian models’ systematic positive bias, which was artificially inflated by the undercatch, is reduced by the adjustment, to attain neutral bias. These YOPP dedicated intense verification activities have identified some strengths, weaknesses and systematic behaviours of the Canadian deterministic prediction systems at high latitudes: these results can serve as a benchmark, for comparison and further development. Moreover, this YOPP verification exercise has revealed some issues related to the verification of surface variables and has led to the development of better verification practices for the polar regions (and beyond).

Verification of ERA5 and ERA-Interim precipitation over Africa at intra-annual and interannual timescales

Over data sparse regions such as Africa, reanalysis datasets can provide a useful alternative to enable climate investigations across a range of temporal scales. Here, a detailed comparison of ERA5 and ERA-Interim precipitation over Africa is done at annual and seasonal temporal scales, over various subregions, to identify biases. Additionally, the representation of interannual variability in the reanalyses is evaluated with an emphasis on the ENSO teleconnection to Africa. Overall, there have been several substantial improvements in ERA5 compared to ERA-Interim. ERA-Interim's wet bias in both the annual and seasonal rainfall over tropical Africa has been substantially reduced in ERA5, which is also evident in the area-averaged bias over Africa (ERA5 = 39.097, ERA-Interim = 91.734). ERA5's representation of the annual cycle has improved over most selected subregions, notably over the southwestern Cape (rERA5=0.993, rERAI=0.988) and Cape South Coast (rERA5=0.917, rERAI=0.859) of South Africa, as well as over the Southern Tropics (rERA5=0.998, rERAI=0.971). ERA5 shows higher correlations with observations in the spatial patterns of interannual variability across all seasons, but particularly DJF (Spearman's rERA5=0.504, rERAI=0.475) and MAM (Spearman's rERA5=0.488, rERAI=0.435). Finally, both models capture the ENSO signal remarkably well, although the spatial extent of positive/negative anomalies could be improved in some seasons and regions.

A New Spatial Distance Metric for Verification of Precipitation

Precipitation is an essential meteorological variable affecting the biosphere and human societies. At the same time, precipitation is notoriously difficult to predict and verify. A new spatial distance metric for verification of precipitation is presented. It is called the Precipitation Smoothing Distance (PSD). The aim was to develop a measure that would provide a good and meaningful approximation of the displacement of precipitation events in the two fields. An estimate of spatial displacement is very appealing for forecast interpretation because it is easy to understand and mimics how humans tend to judge fields by eye. Contrary to most other distance metrics, the new metric does not require thresholding and can thus be used to analyze binary and non-binary fields (e.g., continuous or multi-level). The analysis of idealized situations showed that the new metric provides a meaningful approximation of the displacement. Typically the estimate of displacement provided by PSD was better than the results provided by most other metrics. The measure is also not overly sensitive to noise, its results are directly related to the actual displacements of precipitation events, and the events with a larger magnitude have a bigger influence on the resulting value. The analysis of ECMWF precipitation forecasts over Europe and North Africa confirmed that the new metric provides a meaningful approximation of the displacement even in more complex real-world situations.

A Comparison of Methods to Sample Model Errors for Convection-Allowing Ensemble Forecasts in the Setting of Multiscale Initial Conditions Produced by the GSI-Based EnVar Assimilation System

AbstractA gridpoint statistical interpolation (GSI)-based hybrid ensemble–variational (EnVar) scheme was extended for convective scales—including radar reflectivity assimilation—and implemented in real-time spring forecasting experiments. This study compares methods to address model error during the forecast under the context of multiscale initial condition error sampling provided by the EnVar system. A total of 10 retrospective cases were used to explore the optimal design of convection-allowing ensemble forecasts. In addition to single-model single-physics (SMSP) configurations, ensemble forecast experiments compared multimodel (MM) and multiphysics (MP) approaches. Stochastic physics was also applied to MP for further comparison. Neighborhood-based verification of precipitation and composite reflectivity showed each of these model error techniques to be superior to SMSP configurations. Comparisons of MM and MP approaches had mixed findings. The MM approach had better overall skill in heavy-precipitation forecasts; however, MP ensembles had better skill for light (2.54 mm) precipitation and reduced ensemble mean error of other diagnostic fields, particularly near the surface. The MM experiment had the largest spread in precipitation, and for most hours in other fields; however, rank histograms and spaghetti contours showed significant clustering of the ensemble distribution. MP plus stochastic physics was able to significantly increase spread with time to be competitive with MM by the end of the forecast. The results generally suggest that an MM approach is best for early forecast lead times up to 6–12 h, while a combination of MP and stochastic physics approaches is preferred for forecasts beyond 6–12 h.

Verification of high resolution (12 km) Global Ensemble Prediction System

Abstract Ensemble Prediction Systems (EPSs) are used to estimate the uncertainty in a weather forecast as well as the most likely outcome. To improve weather forecasts and services, the global EPS at the NCMRWF has been upgraded to 12 km horizontal resolution (with 22 perturbed +1 control members). Although the upgraded version of EPS had become operational from 1st June 2018 in the newly acquired high-performance computing system (HPCS), the previous EPS at 33 km horizontal resolution (with 44 perturbed +1 control members) was also running in the old HPCS till 16th July 2018. As compared to the old EPS in NCMRWF, the new EPS includes perturbations in initial state of sea surface temperature, soil moisture content and deep soil temperature. The analysis of the new EPS also includes some more sources of observations. The perturbed 22 members long forecast of the new EPS provided at 00 UTC is the combination of 11 members from 00 UTC cycle and lagged 11 members from 12 UTC cycle of previous day. In the present study, the performances of the new and old EPS are compared for the period from 1st June to 16th July 2018. Performances of both the systems are assessed considering different attributes of probabilistic forecasting. The verification of precipitation, geopotential height at 500 hPa and zonal wind at 850 hPa has been carried out over the northern hemisphere, southern hemisphere and a smaller tropical region which includes India. The metrics used for verification are ensemble-spread, ensemble root-mean-square error, brier score, brier skill score, relative operating characteristics, reliability, sharpness diagrams, rank histogram, ranked probability score, continuous ranked probability score and continuous ranked probability skill score. Two heavy rainfall events have also been considered for subjective comparison of probabilistic prediction capabilities of both the systems. The results show that the new EPS is more skilful in probabilistic forecasts especially over the Indian region and it significantly outperforms the old EPS in probabilistic forecasts of the selected extreme weather events.

Extreme rainfall sensitivity in convective‐scale ensemble modelling over Singapore

A convective‐scale ensemble system was developed to predict the occurrence of heavy convective rainfall around Singapore with a focus on the prediction of high‐impact events. The new ensemble SINGV‐EPS has been nested within two global ensembles, MOGREPS‐G (UK Met Office) and EC‐ENS (ECMWF). Predicting the occurrence of convective rainfall in an area such as Singapore is challenging and this article discusses the use of the convection‐permitting ensemble to characterize the uncertainties in the prediction of such localized heavy rainfall. First, verification of wind, temperature, and precipitation is performed for a month‐long period to assess the relative performance of each ensemble. This reveals differences, but no robust signal to say one is better than the other. The results are not statistically significant and not all variables are consistently better with one ensemble or the other. Secondly, the precipitation characteristics of SINGV‐EPS are analysed from probabilities of precipitation and variability among the ensemble members. SINGV‐EPS is sensitive to the choice of the global ensemble providing the initial conditions and boundaries. The results suggest there is benefit, in some cases, from combining the two ensembles. Thirdly, the spread of the ensemble precipitation is analysed using the dispersion Fractions Skill Score (dFSS). We compare the impact of the initial perturbations and the perturbations in lateral boundary conditions in both nesting options. The initial perturbations dominate in the beginning of the forecasts, with influence up to T+24 h, and are associated with an upscale growth of the uncertainties. The impact of the parent ensemble and lateral boundary conditions dominate at the end of the forecast and tend to influence larger scales more.

A Comparative Verification of Raw and Bias-Corrected ECMWF Seasonal Ensemble Precipitation Reforecasts in Java (Indonesia)

AbstractDynamical seasonal forecasts are afflicted with biases, including seasonal ensemble precipitation forecasts from the new ECMWF seasonal forecast system 5 (SEAS5). In this study, biases have been corrected using empirical quantile mapping (EQM) bias correction (BC). We bias correct SEAS5 24-h rainfall accumulations at seven monthly lead times over the period 1981–2010 in Java, Indonesia. For the observations, we have used a new high-resolution (0.25°) land-only gridded rainfall dataset [Southeast Asia observations (SA-OBS)]. A comparative verification of both raw and bias-corrected reforecasts is performed using several verification metrics. In this verification, the daily rainfall data were aggregated to monthly accumulated rainfall. We focus on July, August, and September because these are agriculturally important months; if the rainfall accumulation exceeds 100 mm, farmers may decide to grow a third rice crop. For these months, the first 2-month lead times show improved and mostly positive continuous ranked probability skill scores after BC. According to the Brier skill score (BSS), the BC reforecasts improve upon the raw reforecasts for the lower precipitation thresholds at the 1-month lead time. Reliability diagrams show that the BC reforecasts have good reliability for events exceeding the agriculturally relevant 100-mm threshold. A cost/loss analysis, comparing the potential economic value of the raw and BC reforecasts for this same threshold, shows that the value of the BC reforecasts is larger than that of the raw ones, and that the BC reforecasts have value for a wider range of users at 1- to 7-month lead times.

Spatial spread‐skill relationship in terms of agreement scales for precipitation forecasts in a convection‐allowing ensemble

Verification of precipitation is one of the major issues in evaluating numerical weather prediction. In this study, a recently developed neighbourhood‐based method in terms of agreement scales is applied to characterize the scale‐dependent spatial spread‐skill relationship of precipitation forecasts in a 3 km convection‐allowing ensemble prediction system (EPS) over the Yangtze‐Huaihe river basin of China. Thirty cases during the Meiyu season of 2013 are classified into two weather regimes, large coverage (LC) and small coverage (SC), based on the precipitation fractional coverage. Overall, precipitation distributions for these two weather regimes are reasonably forecast by the EPS. The results show that the spatial spread‐skill relationship depends highly on the weather regime. The spatial spread‐skill relationship under SC is poorer and shows more diurnal variation compared to that under LC. In addition, this article extends the neighbourhood‐based method to investigate the relative influence of precipitation intensity and placement on the spatial spread‐skill relationship. With increasing precipitation threshold, the relative impact of precipitation intensity on the relationship gradually decreases, and the influence of precipitation placement becomes dominant.

Design and Implementation of a GSI-Based Convection-Allowing Ensemble-Based Data Assimilation and Forecast System for the PECAN Field Experiment. Part II: Overview and Evaluation of a Real-Time System

Abstract Multiscale ensemble-based data assimilation and forecasts were performed in real time during the Plains Elevated Convection At Night (PECAN) field experiment. A 20-member ensemble of forecasts at 4-km grid spacing was initialized daily at both 1300 and 1900 UTC, together with a deterministic forecast at 1-km grid spacing initialized at 1300 UTC. The configuration of the GSI-based data assimilation and forecast system was guided by results presented in Part I of this two-part study. The present paper describes the implementation of the real-time system and the extensive forecast products that were generated to support the unique interests of PECAN researchers. Subjective and objective verification of the real-time forecasts from 1 June through 15 July 2015 is conducted, with an emphasis on nocturnal mesoscale convective systems (MCSs), nocturnal convective initiation (CI), nocturnal low-level jets (LLJs), and bores on the nocturnal stable layer. Verification of nocturnal precipitation during overnight hours, a proxy for MCSs, shows both greater skill and spread for the 1300 UTC forecasts than the 1900 UTC forecasts. Verification against observed soundings reveals that the forecast LLJs systematically peak, veer, and dissipate several hours before the observations. Comparisons with bores that passed over an Atmospheric Emitted Radiance Interferometer reveal an ability to predict borelike features that is greatly improved at 1-km, compared with 4-km, grid spacing. Objective verification of forecast CI timing reveals strong sensitivity to the PBL scheme but an overall unbiased ensemble.

Predictability of convective precipitation for West Africa: verification of convection-permitting and global ensemble simulations

Within the framework of this investigation, convection-permitting (CP) ensemble forecasts were generated for West Africa by combining different initial and lateral boundary conditions (IBCs) with perturbations that address the uncertainty of land-surface atmosphere interactions (land-surface perturbations). For a multi-analysis setup, IBCs were taken from model analyses of different global models; for a single-model setup, they were selected from the ensemble system of the European Centre for Medium-range Weather Forecasts (ECMWF). The different ensemble setups were assessed using common probabilistic scores as well as by spatial forecast verification of precipitation generated mainly by convective systems during the West African monsoon season. Additionally, it was investigated whether the CP ensemble forecasts were superior to the ECMWF ensemble forecasts.Probabilistic scores were higher for the single-model ensemble than for the multi-analysis setup, but the latter displayed a larger dispersion and more extreme scenarios. From this, it is concluded that the different model analyses can differ strongly from each other. The land-surface perturbations were able to generate sufficient complementary spread. While the CP simulations showed a stronger negative precipitation bias in the southernmost region near the Guinean coast, the ECMWF simulations exhibited a negative bias further north in the Sahel region, where larger convective systems occur less frequently. Not in all cases did the CP ensemble versions produce better probabilistic scores than the global ensemble forecasts, but they yielded larger spread and less underdispersion. Rank histograms, though, were also influenced by the different structure of the precipitation patterns of the global and CP forecasts. Scores improved when using a later version of the CP model as well as with the skill of the global ensemble forecasts used as IBCs. Altogether, the proposed realization of CP ensemble forecasts is found to be suited for the prediction of convective precipitation in West Africa.

Design and Implementation of a GSI-Based Convection-Allowing Ensemble Data Assimilation and Forecast System for the PECAN Field Experiment. Part I: Optimal Configurations for Nocturnal Convection Prediction Using Retrospective Cases

Abstract A real-time GSI-based and ensemble-based data assimilation (DA) and forecast system was implemented at the University of Oklahoma during the 2015 Plains Elevated Convection at Night (PECAN) experiment. Extensive experiments on the configuration of the cycled DA and on both the DA and forecast physics ensembles were conducted using retrospective cases to optimize the system design for nocturnal convection. The impacts of radar DA between 1200 and 1300 UTC, as well as the frequency and number of DA cycles and the DA physics configuration, extend through the following night. Ten-minute cycling of radar DA leads to more skillful forecasts than both more and less frequent cycling. The Thompson microphysics scheme for DA better analyzes the effects of morning convection on environmental moisture than WSM6, which improves the convection forecast the following night. A multi-PBL configuration during DA leads to less skillful short-term forecasts than even a relatively poorly performing single-PBL scheme. Deterministic and ensemble forecast physics configurations are also evaluated. Thompson microphysics and the Mellor–Yamada–Nakanishi–Niino (MYNN) PBL provide the most skillful nocturnal precipitation forecasts. A well thought out multiphysics configuration is shown to provide advantages over evenly distributing three of the best-performing microphysics and PBL schemes or a fixed MYNN/Thompson ensemble. This is shown using objective and subjective verification of precipitation and nonprecipitation variables, including convective initiation. Predictions of the low-level jet are sensitive to the PBL scheme, with the best scheme being variable and time dependent. These results guided the implementation and verification of a real-time ensemble DA and forecast system for PECAN.

Quantitative modeling and experimental verification of carbide precipitation in a martensitic Fe–0.16 wt%C–4.0 wt%Cr alloy

Precipitation of carbides during tempering of a martensitic Fe–0.16wt% C–4.0wt% Cr alloy has been investigated by experimental analysis and quantitative modeling. It is found that both M7C3 and M23C6 form, at low- and high-angle grain boundaries in the martensite, as well as, at dislocations inside individual laths of martensite, during tempering at 700°C. The applied Kampmann–Wagner numerical (KWN) modeling, utilizing CALPHAD thermodynamic and kinetic databases together with an assumption of local equilibrium and a constant tie-line, captures the main features of the precipitation, with a transient formation of metastable M23C6, and with M7C3 as the stable carbide. The predicted volume fraction and size are in reasonable agreement with extraction experiments for M7C3. However, for the metastable minority carbide M23C6, the modeling underestimates the size and overestimates the volume fraction within the transient time. With sound thermodynamic databases and physical parameter input, the adopted simplified modeling scheme is a valuable tool for materials design and optimization. Furthermore, by treating conditions at the phase interface more rigorously it is possible to account for different mechanisms of precipitation, such as e.g., non-partitioning local equilibrium, which could be important in systems where interstitial elements diffuse much faster than the substitutional ones.

Verification of Quantitative Precipitation Reforecasts over the Southeastern United States

Abstract NOAA’s second-generation reforecasts are approximately consistent with the operational version of the 2012 NOAA Global Ensemble Forecast System (GEFS). The reforecasts allow verification to be performed across a multidecadal time period using a static model, in contrast to verifications performed using an ever-evolving operational modeling system. This contribution examines three commonly used verification metrics for reforecasts of precipitation over the southeastern United States: equitable threat score, bias, and ranked probability skill score. Analysis of the verification metrics highlights the variation in the ability of the GEFS to predict precipitation across amount, season, forecast lead time, and location. Beyond day 5.5, there is little useful skill in quantitative precipitation forecasts (QPFs) or probabilistic QPFs. For lighter precipitation thresholds [e.g., 5 and 10 mm (24 h)−1], use of the ensemble mean adds about 10% to the forecast skill over the deterministic control. QPFs have increased in accuracy from 1985 to 2013, likely due to improvements in observations. Results of this investigation are a first step toward using the reforecast database to distinguish weather regimes that the GEFS typically predicts well from those regimes that the GEFS typically predicts poorly.

Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 2. Streamflow verification

Summary Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas–Red Basin RFC, the Colorado Basin RFC, the California–Nevada RFC and the Middle Atlantic RFC. In a companion paper, temperature and precipitation hindcasts were produced with the Meteorological Ensemble Forecast Processor (MEFP) and verified against observed temperature and precipitation, respectively. Inputs to the MEFP comprised raw precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (MEFP-GFS) and a conditional or “resampled” climatology (MEFP-CLIM). For this paper, streamflow hindcasts were produced with the Community Hydrologic Prediction System and were bias-corrected with the Ensemble Post-processor (EnsPost). In order to separate the meteorological and hydrologic uncertainties, the raw streamflow forecasts were verified against simulated streamflows, as well as observed flows. Also, when verifying the bias-corrected streamflow forecasts, the total skill was decomposed into contributions from the MEFP-GFS and the EnsPost. In general, the streamflow forecasts are substantially more skillful when using the MEFP-GFS together with the EnsPost than using the MEFP with resampled climatology alone. However, both the raw and bias-corrected streamflow forecasts have lower biases, stronger correlations and are more skillful in CB- and CN-RFCs than AB- and MA-RFCs. In addition, there are strong variations in forecast quality with streamflow amount, forecast lead time, season and aggregation period. The relative importance of the meteorological and hydrologic uncertainties also varies between basins and is modulated by the same controls on forecast quality. For example, the MEFP-GFS accounts for the majority of skill in the CNRFC basins. This is associated with the greater predictability of large storms in the North Coast Ranges during the winter months. In CBRFC, much of the skill in the streamflow forecasts originates from the hydrologic modeling and the EnsPost, particularly during the snowmelt period. In AB- and MA-RFCs, the contributions from the MEFP and the EnsPost are more variable. This paper summarizes the verification results, describes the expected performance and limitations of the HEFS for short- to medium-range streamflow forecasting, and provides recommendations for future research.

Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 1. Experimental design and forcing verification

Summary Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas-Red Basin RFC, the Colorado Basin RFC, the California-Nevada RFC, and the Middle Atlantic RFC. Precipitation and temperature forecasts were produced with the HEFS Meteorological Ensemble Forecast Processor (MEFP). Inputs to the MEFP comprised “raw” precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (GFS) and a climatological ensemble, which involved resampling historical observations in a moving window around the forecast valid date (“resampled climatology”). In both cases, the forecast horizon was 1–14 days. This paper outlines the hindcasting and verification strategy, and then focuses on the quality of the temperature and precipitation forecasts from the MEFP. A companion paper focuses on the quality of the streamflow forecasts from the HEFS. In general, the precipitation forecasts are more skillful than resampled climatology during the first week, but comprise little or no skill during the second week. In contrast, the temperature forecasts improve upon resampled climatology at all forecast lead times. However, there are notable differences among RFCs and for different seasons, aggregation periods and magnitudes of the observed and forecast variables, both for precipitation and temperature. For example, the MEFP-GFS precipitation forecasts show the highest correlations and greatest skill in the California Nevada RFC, particularly during the wet season (November–April). While generally reliable, the MEFP forecasts typically underestimate the largest observed precipitation amounts (a Type-II conditional bias). As a statistical technique, the MEFP cannot detect, and thus appropriately correct for, conditions that are undetected by the GFS. The calibration of the MEFP to provide reliable and skillful forecasts of a range of precipitation amounts (not only large amounts) is a secondary factor responsible for these Type-II conditional biases. Interpretation of the verification results leads to guidance on the expected performance and limitations of the MEFP, together with recommendations on future enhancements.

Reproducibility of Present Climate in a Non-Hydrostatic Regional Climate Model Nested within an Atmosphere General Circulation Model

A 20-year-integration of a non-hydrostatic regional climate model (NHRCM) was conducted to reproduce the present climate. The annual mean surface temperature and precipitation of Japan are well simulated in NHRCM. There was one conspicuous outlier in the verification of the model's annual precipitation with observations. An even finer grid spacing than 5 km would be required to reproduce the precipitation at this observation point. The regional model successfully simulated heavy precipitation that a general circulation model could not reproduce. This model reproduced snow depth well in most of Japan, except in the north on the coast of the Sea of Japan, where the depth was underestimated.

Object-Based Analysis and Verification of WRF Model Precipitation in the Low- and Midlatitude Pacific Ocean

Abstract An extended version of the Method for Object-based Diagnostic Evaluation (MODE) was used to perform a verification of precipitation provided by the Weather Research and Forecasting (WRF) model Tropical Channel Simulation (performed by NCAR). Model 3-hourly precipitation accumulations were compared to the Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite-derived precipitation in the low- and midlatitude Pacific Ocean during 1998–2000. Overall, the spatial distribution of annual mean precipitation (i.e., the clear shape of the ITCZ with two maxima—one located in the east and one in the west) was reproduced well by the WRF model; however, there was considerably more precipitation in the WRF simulation than in the TRMM dataset. Object-based analysis identified more precipitation objects with life spans less than 30 h and fewer objects with life spans longer than 60 h in the TRMM dataset compared to the WRF simulation. Objects with the longest life span (>90 h) tended to occur in similar regions in both the modeled and satellite-derived datasets. Two of these regions were in the western part of the domain and one was in the eastern portion of the ITCZ, although these regions did not necessarily coincide with the regions of maximum precipitation accumulations. Both datasets had mostly eastward movement in the midlatitudes. The datasets, however, diverged around the central part of the ITCZ where the TRMM dataset displayed both eastward and westward movement, while westward movement was dominant in the WRF simulation. The analysis also showed that precipitation object trajectories were smoother in the model than in the TRMM dataset.

Preface "Observation, Prediction and Verification of Precipitation (EGU Session 2008)"

Preface "Observation, Prediction and Verification of Precipitation (EGU Session 2008)"

Preface "Observation, Prediction and Verification of Precipitation (EGU Session 2007)"

Preface "Observation, Prediction and Verification of Precipitation (EGU Session 2007)"

Short range forecast verification of convective rain for a night-time event over the area of Cyprus

Night-time rain showers that occurred over the island of Cyprus and surrounding sea during the night of 11 to 12 February 2002 were investigated by means of two numerical weather prediction (NWP) models, one hydrostatic and the other one non-hydrostatic, and two kinds of radar, one ground-based located at Kykkos mountains in Cyprus and the other one on board the Tropical Rainfall Measuring Mission (TRMM) satellite. Verification of forecast precipitation was performed using a combination of radar precipitation estimates. Because of the short time used for verification and due to the mostly convective type of precipitation, it proved difficult to accurately forecast precipitation from the model. Different initial conditions were provided to the models, in an attempt to realize their impact on the prediction of precipitation. Even though skill scores of models are not very high, the non-hydrostatic model shows a better agreement with observations when the starting time of forecast is not too distant from the occurrence of the event.