Search

Multi-model ensemble forecasts are obtained by weighting multiple General Circulation Model (GCM) outputs to heighten forecast skill and reduce uncertainties. The North American Multi-Model Ensemble (NMME) project facilitates the development of such multi-model forecasting schemes by providing publicly-available hindcasts and forecasts online. Here, temperature and precipitation forecasts are enhanced by leveraging the strengths of eight NMME GCMs (CCSM3, CCSM4, CanCM3, CanCM4, CFSv2, GEOS5, GFDL2.1, and FLORb01) across all forecast months and lead times, for four broad climatic European regions: Temperate, Mediterranean, Humid-Continental and Subarctic-Polar. We compare five different approaches to multi-model weighting based on the equally weighted eight single-model ensembles (EW-8), Bayesian updating (BU) of the eight single-model ensembles (BU-8), BU of the 94 model members (BU-94), BU of the principal components of the eight single-model ensembles (BU-PCA-8) and BU of the principal components of the 94 model members (BU-PCA-94). We assess the forecasting skill of these five multi-models and evaluate their ability to predict some of the costliest historical droughts and floods in recent decades. Results indicate that the simplest approach based on EW-8 preserves model skill, but has considerable biases. The BU and BU-PCA approaches reduce the unconditional biases and negative skill in the forecasts considerably, but they can also sometimes diminish the positive skill in the original forecasts. The BU-PCA models tend to produce lower conditional biases than the BU models and have more homogeneous skill than the other multi-models, but with some loss of skill. The use of 94 NMME model members does not present significant benefits over the use of the 8 single model ensembles. These findings may provide valuable insights for the development of skillful, operational multi-model forecasting systems. [ABSTRACT FROM AUTHOR]

Precipitation is still one of the most complex climate variables to observe, to understand, and to handle within climate monitoring and climate analysis as well as to simulate in numerical weather prediction and climate models. Especially over ocean, less is known about precipitation than over land due to the sparsity of in situ observations. Here, we introduce and discuss a global Expert Team on Climate Change and Indices (ETCCDI)-based precipitation climatology. The basis for computation of this climatology is the global precipitation dataset Daily Precipitation Analysis for Climate Prediction (DAPACLIP) which combines in situ observation data over land and satellite-based remote sensing data over ocean in daily temporal resolution, namely data from the Global Precipitation Climatology Centre (GPCC) and the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) dataset. The DAPACLIP dataset spans the period 1988-2008 and thus the global ETCCDI-based precipitation climatology covers 21 years in total. Regional aspects of the climatology are also discussed with focus on Europe and the monsoon region of south-east Asia. To our knowledge, this is the first presentation and discussion of an ETCCDI-based precipitation climatology on a global scale. [ABSTRACT FROM AUTHOR]

In ensemble-based assimilation schemes for cloud-resolving models (CRMs), the precipitation-related variables have serious sampling errors. The purpose of the present study is to examine the sampling error properties and the forecast error characteristics of the operational CRM of the Japan Meteorological Agency (JMANHM) and to develop a sampling error damping method based on the CRM forecast error characteristics. The CRM forecast error was analyzed for meteorological disturbance cases using 100-member ensemble forecasts of the JMANHM. The ensemble forecast perturbation correlation had a significant noise associated with the precipitation-related variables, because of sampling errors. The precipitation-related variables were likely to suffer this sampling error in most precipitating areas. An examination of the forecast error characteristics revealed that the CRM forecast error satisfied the assumption of the spectral localization, while the spatial localization with constant scales, or variable localization, were not applicable to the CRM. A neighboring ensemble (NE) method was developed, which was based on the spectral localization that estimated the forecast error correlation at the target grid point, using ensemble members for neighboring grid points. To introduce this method into an ensemble-based variational assimilation scheme, the present study horizontally divided the NE forecast error into large-scale portions and deviations. As single observation assimilation experiments showed, this 'dual-scale NE' method was more successful in damping the sampling error and generating plausible, deep vertical profile of precipitation analysis increments, compared to a simple spatial localization method or a variable localization method. [ABSTRACT FROM AUTHOR]

Monthly precipitation hindcasts/projections for various climate change scenarios have been available for over a decade. More recently, daily precipitation hindcasts/projections using downscaled bias-corrected constructed analog (BCCA) have garnered wide usage for climate change investigations. Dry bias and drizzle effect reported in previous studies are among known limitations of downscaled BCCA precipitation hindcasts/projections which affect end-user applications. End users also require a reliable representation of precipitation sequence as well as physical consistency of precipitation series for climate change impact assessment applications. This study focuses on the evaluation of the daily sequence of downscaled BCCA precipitation hindcasts. Herein, direct use of BCCA daily precipitation hindcasts are further assessed to determine their suitability to study daily soil moisture dynamics at the field-scale for central Oklahoma climatic conditions. Three daily precipitation data sets were considered: (1) the 1961–1999 BCCA precipitation hindcasts for a 12 km grid in central Oklahoma, (2) the 1961–1999 spatially interpolated daily precipitation data used in the BCCA downscaling procedure, and (3) the 1961–1999 observed daily precipitation observations at the Weatherford COOP Weather Station located within the 12 km BCCA grid. The BCCA daily precipitation hindcasts showed a larger number of rainy days, lower rainfall amounts per rainy day, and longer sequences of consecutive rainy-day clusters than found in observations. These differences were large enough to suggest that BCCA daily precipitation hindcasts may not reflect the characteristics of actual precipitation observations at a point location, i.e., weather station. Additional analyses at Walnut Creek, Iowa and Moorhead, Mississippi confirmed the results are not location specific but numerically induced as a result of the downscaling process. The underlying cause for the noted differences was traced back to the differences in spatial scales of the BCCA outputs and observed daily precipitation at a station. Thus, caution is advised to end users using BCCA daily rainfall hindcasts/projections directly in local and field-scale water investigations, particularly for applications requiring reliable representation of precipitation sequence. Alternatively, a statistical downscaling method based on stochastic weather generation that includes wet-day dry-day transition probabilities would provide the desired temporal disaggregation and sequencing of daily rainfall events. [ABSTRACT FROM AUTHOR]

European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis total precipitation estimates are validated against ten years of in situ precipitation measurements onboard of ships over the Baltic Sea. A statistical analysis for binary forecasts and mean rain rates derived from all data show a good agreement with observations. However, a closer look reveals an underestimation of ERA-Interim total precipitation in spring and an overestimation in autumn, obviously related to stability. Deriving stability and evaporation by a bulk flux scheme it could be shown, in fact, that ERA-Interim underestimates precipitation for conditions with low evaporation and strongly overestimates it for conditions with high evaporation. Since ERA-Interim surface fields become too dry with increasing evaporation compared to independent synoptic ship observations, uncertainties in the ECMWF convection scheme may possibly cause these biases in seasonal precipitation. [ABSTRACT FROM AUTHOR]

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society (IRI). In conjunction with the GLM (generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts (the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas. [ABSTRACT FROM AUTHOR]

The climate that has a dynamic structure varies continuously. Basic climatological features of any region depend on many meteorological variables, such as precipitation and temperature records. The changes in these two variables reveal important clues about climate variations. Turkey is one of the sensitive areas for climate variation around the world. In this study, statistical analysis is applied to historical data set consisted of monthly precipitation totals (mm.) which are provided by the Turkish State Meteorological Service(TSMS) for 1999-2010 period in Turkey. Furthermore, a sinusoidal model for each region obtained by using a time series method is proposed in order to obtain a sinusoidal form for the precipitation to each region. Besides a basic form of Frequency Domain Techniques, usual Box-Jenkins ARIMA approach is applied in this study. In conclusion, it is observed that the sinusoidal model predictions are more appropriate than ARIMA techniques and usual generalized linear model (GLM) techniques. [ABSTRACT FROM AUTHOR]

A Bernoulli-generalized Pareto multivariate autoregressive (BMAR) model is proposed in this paper for multisite statistical downscaling of daily precipitation. The proposed model relies on a probabilistic framework to describe the conditional probability density function of precipitation at each station for a given day and handles multivariate dependence in both time and space using a multivariate autoregressive model. Within a probabilistic framework, BMAR employs a regression model whose outputs are parameters of the mixed Bernoulli-generalized Pareto distribution. As a stochastic component, the BMAR employs a latent multivariate autoregressive Gaussian field to preserve lag-0 and lag-1 cross correlations of precipitation at multiple sites. The proposed model is applied for the downscaling of AOGCM data to daily precipitation in the southern part of Québec, Canada. Reanalysis products are used in this study to assess the potential of the proposed method. Based on the mean errors (MEs), the root-mean-square errors (RMSEs), precipitation indices, and the ability to preserve lag-0 and lag-1 cross correlation, results of the study indicate the superiority of the proposed model over a multivariate multiple linear regression (MMLR) model and a multisite hybrid statistical downscaling procedure that combines MMLR and stochastic generator schemes. [ABSTRACT FROM AUTHOR]

Two approaches for accounting for errors in quantitative precipitation forecasts (QPFs) due to uncertainty in the microphysics (MP) parameterization in a convection-allowing ensemble are examined. They include mixed MP (MMP) composed mostly of double-moment schemes and perturbing parameters within the Weather Research and Forecasting single-moment 6-class microphysics scheme (WSM6) MP scheme (PPMP). Thirty-five cases of real-time storm-scale ensemble forecasts produced by the Center for Analysis and Prediction of Storms during the NOAA Hazardous Weather Testbed 2011 Spring Experiment were examined. The MMP ensemble had better fractions Brier scores (FBSs) for most lead times and thresholds, but the PPMP ensemble had better relative operating characteristic (ROC) scores for higher precipitation thresholds. The pooled ensemble formed by randomly drawing five members from the MMP and PPMP ensembles was no more skillful than the more accurate of the MMP and PPMP ensembles. Significant positive impact was found when the two were combined to form a larger ensemble. The QPF and the systematic behaviors of derived microphysical variables were also examined. The skill of the QPF among different members depended on the thresholds, verification metrics, and forecast lead times. The profiles of microphysics variables from the double-moment schemes contained more variation in the vertical than those from the single-moment members. Among the double-moment schemes, WDM6 produced the smallest raindrops and very large number concentrations. Among the PPMP members, the behaviors were found to be consistent with the prescribed intercept parameters. The perturbed intercept parameters used in the PPMP ensemble fell within the range of values retrieved from the double-moment schemes. [ABSTRACT FROM AUTHOR]

This study introduces a concept of global versus local forecast skill of the Madden-Julian oscillation (MJO). The global skill, measured by a commonly used MJO index [the Real-time Multivariate MJO (RMM)], evaluates the model's capability of forecasting global patterns of the MJO, with an emphasis on the zonal wind fields. The local skill is measured by a method of tracking the eastward propagation of MJO precipitation. It provides quantitative information of the strength, propagation speed, and timing of MJO precipitation in a given region, such as the Indian Ocean. Both global and local MJO forecast skills are assessed for ECMWF forecasts of three MJO events during the 2011-12 Dynamics of the MJO (DYNAMO) field campaign. Characteristics of error growth differ substantially between global and local MJO forecast skills, and between the three MJO quantities (strength, speed, and timing) of the local skill measure. They all vary considerably among the three MJO events. Deterioration in global forecast skill for these three events appears to be related to poor local skill in forecasting the propagation speed of MJO precipitation. The global and local MJO forecast skill measures are also applied to evaluate numerical experiments of observation denial, humidity relaxation, and forcing by daily perturbations in sea surface temperature (SST). The results suggest that forecast skill or errors of convective initiation of the three MJO events have global origins. Effects of local (Indian Ocean) factors, such as enhanced observations in the initial conditions, variability of tropospheric humidity and tropical SST, on forecasts of MJO initiation and propagation are limited. [ABSTRACT FROM AUTHOR]

Highly uncertain precipitation input greatly reduces the overall reliability of rainfall-runoff modeling for estimating stream flows, which in turn forms the basis for decisions leading towards sustainable management of water resources. Probabilistic weather forecasting using multiple numerical weather prediction (NWP) models known as ensemble prediction system (EPS), has gained popularity amongst meteorologists in recent years. However, keeping in mind the computational burden and complexities of hydrologic modeling, scientists are constrained to use a limited number of ensemble members to drive the operational river flow forecasting systems. In this paper, 12 weather ensembles of different size are evaluated for their performance in forecasting occurrence of precipitation over a wet month in a large New Zealand catchment by comparing them against point observations of the corresponding events at rain gauge stations. The authors investigate various configurations of 12-h accumulated precipitation forecasts including members from the grand global ensemble consisting of 179 member forecasts issued at eight data centers connected through the observing research and predictability experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) network. The ensembles are developed using four different techniques including a one-dimensional (1D) clustering approach to group the members into clusters of different sizes in order to study the impact of ensemble size on precipitation forecasting with lead time of 10 days. This approach groups the members into different clusters by minimizing the sum of square distances between the members and the cluster centre. The results indicate that there is generally little improvement in the forecast skill by increasing the ensemble size, and some smaller ensembles can reasonably replace the grand global ensemble without significantly compromising the overall quality of the probabilistic forecast for precipitation occurrence. The results further infer that the grand control ensemble (GCE) combining the control forecasts from all the centers is as good a candidate as the grand global ensemble despite being much smaller in size. [ABSTRACT FROM AUTHOR]

A regional ensemble Kalman filter (EnKF) system is established for potential Rapid Refresh (RAP) operational application. The system borrows data processing and observation operators from the gridpoint statistical interpolation (GSI), and precalculates observation priors using the GSI. The ensemble square root Kalman filter (EnSRF) algorithm is used, which updates both the state vector and observation priors. All conventional observations that are used in the operational RAP GSI are assimilated. To minimize computational costs, the EnKF is run at ⅓ of the operational RAP resolution or about 40-km grid spacing, and its performance is compared to the GSI using the same datasets and resolution. Short-range (up to 18 h, the RAP forecast length) forecasts are verified against soundings, surface observations, and precipitation data. Experiments are run with 3-hourly assimilation cycles over a 9-day convectively active retrospective period from spring 2010. The EnKF performance was improved by extensive tuning, including the use of height-dependent covariance localization scales and adaptive covariance inflation. When multiple physics parameterization schemes are employed by the EnKF, forecast errors are further reduced, especially for relative humidity and temperature at the upper levels and for surface variables. The best EnKF configuration produces lower forecast errors than the parallel GSI run. Gilbert skill scores of precipitation forecasts on the 13-km RAP grid initialized from the 3-hourly EnKF analyses are consistently better than those from GSI analyses. [ABSTRACT FROM AUTHOR]

Probabilistic quantitative precipitation forecasts (PQPFs) were generated from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database from July to October 2010 using data from Europe (ECMWF), the United Kingdom [Met Office (UKMO)], the United States (NCEP), and Canada [Canadian Meteorological Centre (CMC)]. Forecasts of 24-h accumulated precipitation were evaluated at 1° grid spacing within the contiguous United States against analysis data based on gauges and bias-corrected radar data. PQPFs from ECMWF's ensembles generally had the highest skill of the raw ensemble forecasts, followed by CMC. Those of UKMO and NCEP were less skillful. PQPFs from CMC forecasts were the most reliable but the least sharp, and PQPFs from NCEP and UKMO ensembles were the least reliable but sharper. Multimodel PQPFs were more reliable and skillful than individual ensemble prediction system forecasts. The improvement was larger for heavier precipitation events [e.g., >10 mm (24 h)−1] than for smaller events [e.g., >1 mm (24 h)−1]. ECMWF ensembles were statistically postprocessed using extended logistic regression and the five-member weekly reforecasts for the June-November period of 2002-09, the period where precipitation analyses were also available. Multimodel ensembles were also postprocessed using logistic regression and the last 30 days of prior forecasts and analyses. The reforecast-calibrated ECMWF PQPFs were much more skillful and reliable for the heavier precipitation events than ECMWF raw forecasts but much less sharp. Raw multimodel PQPFs were generally more skillful than reforecast-calibrated ECMWF PQPFs for the light precipitation events but had about the same skill for the higher-precipitation events; also, they were sharper but somewhat less reliable than ECMWF reforecast-based PQPFs. Postprocessed multimodel PQPFs did not provide as much improvement to the raw multimodel PQPF as the reforecast-based processing did to the ECMWF forecast. The evidence presented here suggests that all operational centers, even ECMWF, would benefit from the open, real-time sharing of precipitation forecast data and the use of reforecasts. [ABSTRACT FROM AUTHOR]

A new hydrometeor classification algorithm that combines thermodynamic output from the Rapid Update Cycle (RUC) model with polarimetric radar observations is introduced. The algorithm improves upon existing classification techniques that rely solely on polarimetric radar observations by using thermodynamic information to help to diagnose microphysical processes (such as melting or refreezing) that might occur aloft. This added information is especially important for transitional weather events for which past studies have shown radar-only techniques to be deficient. The algorithm first uses vertical profiles of wet-bulb temperature derived from the RUC model output to provide a background precipitation classification type. According to a set of empirical rules, polarimetric radar data are then used to refine precipitation-type categories when the observations are found to be inconsistent with the background classification. Using data from the polarimetric KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman, Oklahoma, the algorithm is tested on a transitional winter-storm event that produced a combination of rain, freezing rain, ice pellets, and snow as it passed over central Oklahoma on 30 November 2006. Examples are presented in which the presence of a radar bright band (suggesting an elevated warm layer) is observed immediately above a background classification of dry snow (suggesting the absence of an elevated warm layer in the model output). Overall, the results demonstrate the potential benefits of combining polarimetric radar data with thermodynamic information from numerical models, with model output providing widespread coverage and polarimetric radar data providing an observation-based modification of the derived precipitation type at closer ranges. [ABSTRACT FROM AUTHOR]

Heavy-rainfall events are common in southern France and frequently result in devastating flash floods. Thus, an appropriate anticipation of future rainfall is required: for early flood warning, at least 12-24 h in advance; for alerting operational services, at least 2-3 days ahead. Precipitation forecasts are generally provided by numerical weather prediction models (NWP), and their associated uncertainty is generally estimated through an ensemble approach. Precipitation forecasts also have to be adapted to hydrological scales. This study describes an alternative approach to commonly used limited-area models. Probabilistic quantitative precipitation forecasts (PQPFs) are provided through an analog sorting technique, which directly links synoptic-scale NWP output to catchment-scale rainfall probability distributions. One issue concerns the latest developments in implementing a daily version of this technique into operational conditions. It is shown that the obtained PQPFs depend on the meteorological forecasts used for selecting analogous days and that the method has to be reoptimized when changing the source of synoptic forecasts, because of the NWP output uncertainties. Second, an evaluation of the PQPFs demonstrates that the analog technique performs well for early warning of heavy-rainfall events and provides useful information as potential input to a hydrological ensemble prediction system. It is shown that the obtained daily rainfall distributions can be unreliable. A statistical correction of the observed bias is proposed as a function of the no-rain frequency values, leading to a significant improvement in PQPF sharpness. [ABSTRACT FROM AUTHOR]