Forecast Fields Research Articles

Reduction of deoxynivalenol (DON) contamination by improved fungicide use in wheat. Part 2. Farm scale tests with different nozzle types and updating the integrated approach

Fungicidal control of Fusarium head blight (FHB) of wheat with fungicides generally has poor efficacy (0–40%). However, small plot trials prove that a 70–90% reduction in toxin contamination is possible. We compared two variants of side-spraying nozzles with the Turbo FloodJet. The new nozzle combination (QJ 90, TT F, XR B) reduced visual FHB scores by 50% as compared to the standard TeeJet XR nozzles. The fungicide choice is decisive, the best product reduced DON by 81%, the least effective only by 31%. Greater genetic resistance is also decisive, the most resistant cultivar showed a 73% reduction in DON across all treatments. The combined effect of the fungicide + cultivar was 98.5% between the UTC and best fungicide/variety combination (GK Feny/PT) across three years. The new combined nozzle was more effective at the better fungicides containing prothioconazole, metconazole and tebuconazole, at the less effective fungicides its effect was only average. Correlations between small plot (Part 1) and farm tests were r = 0.96 (P = 0.001) for FHB, r = 0.91 (P = 0–001) for FDK, and r = 0.75 (P = 0.02) for DON indicating that small plot results forecast field usefulness and reduction in field control was close to the small plot results for all traits. The heart of integrated plant management (IPM) is the combination of variety resistance, the effective fungicide and the side-spraying technology with appropriate nozzle choice. Resistance governs fungicide reduction, nozzle influence, effect of previous crop and tillage. Susceptible cultivars should be withdrawn from production, but cultivars such as GK Feny treated with preventive fungicides at the flowering phase can be grown without any serious food safety risk. With a careful field-specific IPM combination, the reduction can be doubled without significant additional costs.

Potential Applications of Small Satellite Microwave Observations for Monitoring and Predicting Global Fast-Evolving Weathers

Two new constellations comprising 14 small satellites with microwave instruments onboard are proposed in this study. Properly arranged, the first constellation is capable of covering the entire globe at an hourly interval and the second constellation is more favorable for the tropical area. Compared to the current JPSS and MetOp satellite constellation, which has passive microwave sounding instruments ATMS or AMSU, a small satellite constellation is more cost effective, requires a shorter development cycle, and has a smaller launch-failure impact. For a designated microwave small satellite constellation, the brightness temperature distribution in space and time is simulated using the operational forecast fields as inputs to the Community Radiative Transfer Model (CRTM). It is demonstrated that the structural change of fast-evolving weather systems such as a middle-latitude cyclone can be well captured from small satellite brightness temperatures.

Sensitivity of Dryline Convection Forecasts to Upstream Forecast Errors for Two Weakly Forced MPEX Cases

Abstract The sensitivity of convective forecasts along the Texas dryline to upstream forecast fields at earlier lead times is evaluated for two consecutive days (27–28 May) characterized by no clear synoptic forcing for convection initiation (CI) during the 2013 Mesoscale Predictability Experiment (MPEX) by applying the ensemble-based sensitivity technique to convection-allowing WRF ensemble forecasts. For both cases, the members with stronger convection are characterized by higher water vapor just above the top of the boundary layer, which is associated with lower convective inhibition (CIN) at the time of CI. Forecast convection is sensitive to the lower-tropospheric water vapor and zonal wind at earlier lead times farther south along the dryline, such that increasing the water vapor and/or making the wind more easterly is associated with more convection. For 28 May, the water vapor along the dryline is also sensitive to the convection that occurs around 0600 UTC, which leads to cold pool–induced surface divergence that subsequently shifts the dryline north or south. Ensemble members that correctly have decreased convection in the Texas Panhandle on 28 May have more accurate forecasts of water vapor and meridional wind with respect to dropwindsondes in the sensitive region 9 h prior to CI compared to members with more extensive convection. Reducing the 0-h water vapor within the sensitive region can suppress convection in members with extensive convection; however, increasing the 0-h water vapor does not lead to more convection in members without convection.

A method for preferential selection of dates in the Schaake shuffle approach to constructing spatiotemporal forecast fields of temperature and precipitation

AbstractHydrological forecasts strongly rely on predictions of precipitation amounts and temperature as meteorological forcings for hydrological models. Ensemble weather predictions provide a number of different scenarios that reflect the uncertainty about these meteorological inputs, but these are often biased and under‐dispersive, and therefore require statistical postprocessing. In addition to correcting the marginal distributions of the two weather variables, postprocessing methods must reconstruct their spatial, temporal, and intervariable dependence in order to generate physically realistic forecast trajectories that can be used as forcings of hydrological streamflow forecast models. For many years, a sample reordering method referred to as “Schaake shuffle” has been used successfully to address this multivariate aspect of forecast distributions by using historical observation trajectories as multivariate “dependence templates.” This paper proposes a variant of the Schaake shuffle, in which the historical dates are selected such that the marginal distributions of the corresponding observation trajectories are similar to the forecast marginal distributions, thus making it more likely that spatial and temporal gradients are preserved during the reordering procedure. This new approach is demonstrated with temperature and precipitation forecasts over four river basins in California, and it is shown to improve upon the standard Schaake shuffle both with respect to verification metrics applied to the forcings, and verification metrics applied to the resulting streamflow predictions.

Effect of Enhanced Satellite-Derived Atmospheric Motion Vectors on Numerical Weather Prediction in East Asia Using an Adjoint-Based Observation Impact Method

Abstract When producing forecasts by integrating a numerical weather prediction model from an analysis, not all observations assimilated into the analysis improve the forecast. Therefore, the impact of particular observations on the forecast needs to be evaluated quantitatively to provide relevant information about the impact of the observing system. One way to assess the observation impact is to use an adjoint-based method that estimates the impact of each assimilated observation on reducing the error of the forecast. In this study, the Weather Research and Forecasting Model and its adjoint are used to evaluate the impact of several types of observations, including enhanced satellite-derived atmospheric motion vectors (AMVs) that were made available during observation campaigns for two typhoons: Sinlaku and Jangmi, which both formed in the western North Pacific during September 2008. Without the assimilation of enhanced AMV data, radiosonde observations and satellite radiances show the highest total observation impact on forecasts. When enhanced AMVs are included in the assimilation, the observation impact of AMVs is increased and the impact of radiances is decreased. The highest ratio of beneficial observations comes from GPS Precipitable Water (GPSPW) without the assimilation of enhanced AMVs. Most observations express a ratio of approximately 60%. Enhanced AMVs improve forecast fields when tracking the typhoon centers of Sinlaku and Jangmi. Both the model background and the analysis are improved by the continuous cycling of enhanced AMVs, with a greater reduction in forecast error along the background trajectory than the analysis trajectory. Thus, while the analysis–forecast system is improved by assimilating these observations, the total observation impact is smaller as a result of the improvement.

Optimization and Application Analysis of Artificial Neural Network Based on Improved Particle Swarm Optimization

To deal with the shortcomings of premature convergence and weak local search ability of Particle Swarm Optimization (PSO) algorithm, an improved PSO algorithm is proposed, based on a Lorenz chaotic sequence and weightself-adaptive adjustment. It puts forwards Hybrid Neural Network Model based on LSAPSO algorithm and RBF ANN (HANN), by optimizing the function center, expansion constant, network weights of RBF ANN with LSAPSO algorithm. It establishes a water quality evaluation model based on HANN, and conduct experiments on water quality evaluation with HANN forecast model. The results show that the model has good performance in water quality evaluation, forecast accuracy and correlation. And the HANN water quality evaluation and forecast is feasible and reliable, which can provide a new forecast method for many forecast fields.

Preliminary study on direct assimilation of cloud-affected satellite microwave brightness temperatures

Direct assimilation of cloud-affected microwave brightness temperatures from AMSU-A into the GSI three-dimensional variational (3D-Var) assimilation system is preliminarily studied in this paper. A combination of cloud microphysics parameters retrieved by the 1D-Var algorithm (including vertical profiles of cloud liquid water content, ice water content, and rain water content) and atmospheric state parameters from objective analysis fields of an NWP model are used as background fields. Three cloud microphysics parameters (cloud liquid water content, ice water content, and rain water content) are applied to the control variable. Typhoon Halong (2014) is selected as an example. The results show that direct assimilation of cloud-affected AMSU-A observations can effectively adjust the structure of large-scale temperature, humidity and wind analysis fields due to the assimilation of more AMSU-A observations in typhoon cloudy areas, especially typhoon spiral cloud belts. These adjustments, with temperatures increasing and humidities decreasing in the movement direction of the typhoon, bring the forecasted typhoon moving direction closer to its real path. The assimilation of cloud-affected satellite microwave brightness temperatures can provide better analysis fields that are more similar to the actual situation. Furthermore, typhoon prediction accuracy is improved using these assimilation analysis fields as the initial forecast fields in NWP models.

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

Abstract. Skillful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically derived relationships between IOPs and variables such as chlorophyll-a concentration (Chl a), total suspended solids (TSS) and coloured dissolved organic matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due to the additional signal from bottom reflectance. Rather than assimilate quantities calculated using IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance (RSR). To assimilate the observed RSR, we use an in-water optical model to produce an equivalent simulated RSR and calculate the mismatch between the observed and simulated quantities to constrain the BGC model with a deterministic ensemble Kalman filter (DEnKF). The traditional assumption that simulated surface Chl a is equivalent to the remotely sensed OC3M estimate of Chl a resulted in a forecast error of approximately 75 %. We show this error can be halved by instead using simulated RSR to constrain the model via the assimilation system. When the analysis and forecast fields from the RSR-based assimilation system are compared with the non-assimilating model, a comparison against independent in situ observations of Chl a, TSS and dissolved inorganic nutrients (NO3, NH4 and DIP) showed that errors are reduced by up to 90 %. In all cases, the assimilation system improves the simulation compared to the non-assimilating model. Our approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially derived TSS and CDOM or the lack of a calibrated regional IOP algorithm.

A bias corrected WRF mesoscale fire weather dataset for Victoria, Australia 1972-2012

Climatology data of fire weather across the landscape can provide science-based evidence for informing strategic decisions to ameliorate the impacts (at times extreme) of bushfires on community socio-economic wellbeing and to sustain ecosystem health and functions. A long-term climatology requires spatial and temporal data that are consistent to represent the landscape in sufficient detail to be useful for fire weather studies and management purposes. To address this inhomogeneity problem for analyses of a variety of fire weather interests and to provide a dataset for management decision-support, a homogeneous 41-year (1972-2012), hourly interval, 4 km gridded climate dataset for Victoria has been generated using a combination of mesoscale modelling, global reanalysis data, surface observations, and historic observed rainfall analyses. Hourly near-surface forecast fields were combined with Drought Factor (DF) fields calculated from the Australian Water Availability Project (AWAP) rainfall analyses to generate fields of hourly fire danger indices for each hour of the 41-year period. A quantile mapping (QM) bias correction technique utilizing available observations during 1996-2012 was used to ameliorate any model biases in wind speed, temperature and relative humidity. Extensive evaluation was undertaken including both quantitative and case study qualitative assessments. The final dataset includes 4-km surface hourly temperature, relative humidity, wind speed, wind direction, Forest Fire Danger Index (FFDI), and daily DF and Keetch-Byram Drought Index (KBDI), and a 32-level full three-dimensional volume atmosphere.

Prediction and Ensemble Forecast Verification of Hail in the Supercell Storms of 20 May 2013

AbstractDespite recent advances in storm-scale ensemble NWP, short-term (0–90 min) explicit forecasts of severe hail remain a major challenge as a result of the fast evolution and short time scales of hail-producing convective storms and the substantial uncertainty associated with the microphysical representation of hail. In this study, 0–90-min ensemble hail forecasts for the supercell storms of 20 May 2013 over central Oklahoma are examined and verified, with the goals of 1) evaluating ensemble forecast performance, 2) comparing the advantages and limitations of different forecast fields potentially suitable for the prediction of hail and severe hail in a Warn-on-Forecast setting, and 3) evaluating the use of dual-polarization radar observations for hail forecast validation. To address the challenges of hail prediction and to produce skillful forecasts, the ensemble uses a two-moment microphysics scheme that explicitly predicts a hail-like rimed-ice category and is run with a grid spacing of 500 m. Radar reflectivity factor and radial velocity, along with surface observations, are assimilated every 5 min for 1 h as the storms were developing to maturity, followed by a 90-min ensemble forecast. Several methods of hail prediction and hail forecast verification are then examined, including the prediction of the maximum hail size compared to Storm Prediction Center (SPC) and Meteorological Phenomena Identification Near the Ground (mPING) hail observations, and verification of model data against single- and dual-polarization radar-derived fields including hydrometeor classification algorithm (HCA) output and the maximum estimated size of hail (MESH). The 0–90-min ensemble hail predictions are found to be marginally to moderately skillful depending on the verification method used.

VISIR-I: small vessels – least-time nautical routes using wave forecasts

Abstract. A new numerical model for the on-demand computation of optimal ship routes based on sea-state forecasts has been developed. The model, named VISIR (discoVerIng Safe and effIcient Routes) is designed to support decision-makers when planning a marine voyage. The first version of the system, VISIR-I, considers medium and small motor vessels with lengths of up to a few tens of metres and a displacement hull. The model is comprised of three components: a route optimization algorithm, a mechanical model of the ship, and a processor of the environmental fields. The optimization algorithm is based on a graph-search method with time-dependent edge weights. The algorithm is also able to compute a voluntary ship speed reduction. The ship model accounts for calm water and added wave resistance by making use of just the principal particulars of the vessel as input parameters. It also checks the optimal route for parametric roll, pure loss of stability, and surfriding/broaching-to hazard conditions. The processor of the environmental fields employs significant wave height, wave spectrum peak period, and wave direction forecast fields as input. The topological issues of coastal navigation (islands, peninsulas, narrow passages) are addressed. Examples of VISIR-I routes in the Mediterranean Sea are provided. The optimal route may be longer in terms of miles sailed and yet it is faster and safer than the geodetic route between the same departure and arrival locations. Time savings up to 2.7 % and route lengthening up to 3.2 % are found for the case studies analysed. However, there is no upper bound for the magnitude of the changes of such route metrics, which especially in case of extreme sea states can be much greater. Route diversions result from the safety constraints and the fact that the algorithm takes into account the full temporal evolution and spatial variability of the environmental fields.

Modeling of 137Cs as a Tracer in a Regional Model for the Western Pacific, after the Fukushima–Daiichi Nuclear Power Plant Accident of March 2011

Abstract In this study, results are presented from the first operational ocean tracer dispersion model operated by the National Oceanic and Atmospheric Administration/National Weather Service/National Centers for Environmental Prediction (NOAA/NWS/NCEP). This study addresses the dispersion of radionuclide contaminants after the Fukushima–Daiichi nuclear accident that was triggered by the 11 March 2011 earthquake and tsunami. The tracer capabilities of the Hybrid Coordinate Ocean Model (HYCOM) were used in a regional domain for the northwestern Pacific, with nesting lateral boundary conditions using daily nowcast–forecast fields from the global operational Real-Time Ocean Forecast System (RTOFS-Global), a ° HYCOM global forecast from NCEP, based on data-assimilative ° HYCOM Global Ocean Forecast System (GOFS) analyses from the Naval Research Laboratory/Naval Oceanographic Office (NRL/NAVOCEANO). This regional model, RTOFS Episodic Tracers for a region of the North West Pacific (RTOFS-ET_WPA), was in operation until the beginning of 2014, when the simulated 137Cs concentration was very close to the background level in the Pacific before the accident, which was about 2 Becquerel m−3 [Bq; 1 Becquerel = 1 (nuclear decay) s−1].

Using Reforecasts to Improve Forecasting of Fog and Visibility for Aviation*

Abstract Fifteen years of forecasts from the National Oceanic and Atmospheric Administration’s Second-Generation Global Medium-Range Ensemble Reforecast (GEFS/R) dataset were used to develop a statistical model that generates probabilistic predictions of cloud ceiling and visibility. Four major airports—Seattle–Tacoma International Airport (KSEA), San Francisco International Airport (KSFO), Denver International Airport (KDEN), and George Bush Intercontinental Airport (KIAH) in Houston, Texas—were selected for model training and analysis. Numerous statistical model configurations, including the use of several different machine learning algorithms, input predictors, and internal parameters, were explored and verified through cross validation to develop skillful forecasts at each station. The final model was then compared with both probabilistic climatology-based forecasts and deterministic operational guidance. Results indicated significantly enhanced skill within both deterministic and probabilistic frameworks from the model trained in this study relative to both operational guidance and climatology at all stations. Probabilistic forecasts also showed substantially higher skill within the framework used than any deterministic forecast. Dewpoint depression and cloud cover forecast fields from the GEFS/R model were typically found to have the highest correspondence with observed flight rule conditions of the atmospheric fields examined. Often forecast values nearest the prediction station were not found to be the most important flight rule condition predictors, with forecast values along coastlines and immediately offshore, where applicable, often serving as superior predictors. The effect of training data length on model performance was also examined; it was determined that approximately 3 yr of training data from a dynamical model were required for the statistical model to robustly capture the relationships between model variables and observed flight rule conditions (FRCs).

How essential are Argo observations to constrain a global ocean data assimilation system?

Abstract. Observing system experiments (OSEs) are carried out over a 1-year period to quantify the impact of Argo observations on the Mercator Ocean 0.25° global ocean analysis and forecasting system. The reference simulation assimilates sea surface temperature (SST), SSALTO/DUACS (Segment Sol multi-missions dALTimetrie, d'orbitographie et de localisation précise/Data unification and Altimeter combination system) altimeter data and Argo and other in situ observations from the Coriolis data center. Two other simulations are carried out where all Argo and half of the Argo data are withheld. Assimilating Argo observations has a significant impact on analyzed and forecast temperature and salinity fields at different depths. Without Argo data assimilation, large errors occur in analyzed fields as estimated from the differences when compared with in situ observations. For example, in the 0–300 m layer RMS (root mean square) differences between analyzed fields and observations reach 0.25 psu and 1.25 °C in the western boundary currents and 0.1 psu and 0.75 °C in the open ocean. The impact of the Argo data in reducing observation–model forecast differences is also significant from the surface down to a depth of 2000 m. Differences between in situ observations and forecast fields are thus reduced by 20 % in the upper layers and by up to 40 % at a depth of 2000 m when Argo data are assimilated. At depth, the most impacted regions in the global ocean are the Mediterranean outflow, the Gulf Stream region and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. Therefore, Argo observations matter to constrain the model solution, even for an eddy-permitting model configuration. The impact of the Argo floats' data assimilation on other model variables is briefly assessed: the improvement of the fit to Argo profiles do not lead globally to unphysical corrections on the sea surface temperature and sea surface height. The main conclusion is that the performance of the Mercator Ocean 0.25° global data assimilation system is heavily dependent on the availability of Argo data.

Applications of Bayesian Procrustes shape analysis to ensemble radar reflectivity nowcast verification

This paper introduces the use of Bayesian full Procrustes shape analysis in object-oriented meteorological applications. In particular, the Procrustes methodology is used to generate mean forecast precipitation fields from a set of ensemble forecasts. This approach has advantages over other ensemble averaging techniques in that it can produce a forecast that retains the morphological features of the precipitation structures and present the range of forecast outcomes represented by the ensemble. The production of the ensemble mean avoids the problems of smoothing that result from simple pixel or cell averaging, while producing credible sets that retain information on ensemble spread. Also in this paper, the full Bayesian Procrustes scheme is used as an object verification tool for precipitation forecasts. This is an extension of a previously presented Procrustes shape analysis based verification approach into a full Bayesian format designed to handle the verification of precipitation forecasts that match objects from an ensemble of forecast fields to a single truth image. The methodology is tested on radar reflectivity nowcasts produced in the Warning Decision Support System — Integrated Information (WDSS-II) by varying parameters in the K-means cluster tracking scheme.

Impact of variational assimilation using multivariate background error covariances on the simulation of monsoon depressions over India

Abstract. The background error covariance structure influences a variational data assimilation system immensely. The simulation of a weather phenomenon like monsoon depression can hence be influenced by the background correlation information used in the analysis formulation. The Weather Research and Forecasting Model Data assimilation (WRFDA) system includes an option for formulating multivariate background correlations for its three-dimensional variational (3DVar) system (cv6 option). The impact of using such a formulation in the simulation of three monsoon depressions over India is investigated in this study. Analysis and forecast fields generated using this option are compared with those obtained using the default formulation for regional background error correlations (cv5) in WRFDA and with a base run without any assimilation. The model rainfall forecasts are compared with rainfall observations from the Tropical Rainfall Measurement Mission (TRMM) and the other model forecast fields are compared with a high-resolution analysis as well as with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis. The results of the study indicate that inclusion of additional correlation information in background error statistics has a moderate impact on the vertical profiles of relative humidity, moisture convergence, horizontal divergence and the temperature structure at the depression centre at the analysis time of the cv5/cv6 sensitivity experiments. Moderate improvements are seen in two of the three depressions investigated in this study. An improved thermodynamic and moisture structure at the initial time is expected to provide for improved rainfall simulation. The results of the study indicate that the skill scores of accumulated rainfall are somewhat better for the cv6 option as compared to the cv5 option for at least two of the three depression cases studied, especially at the higher threshold levels. Considering the importance of utilising improved flow-dependent correlation structures for efficient data assimilation, the need for more studies on the impact of background error covariances is obvious.

Bias characterization of CrIS shortwave temperature sounding channels using fast NLTE model and GFS forecast field

AbstractThe strong nonlocal thermal equilibrium (NLTE) emission in the upper atmosphere impedes the usage of Cross‐track Infrared Sounder (CrIS) temperature channels near 4.3 µm in the operational data assimilation. This study explores the bias characteristics of those temperature channels near 4.3 µm with and without a fast NLTE model implemented in the Community Radiative Transfer Model (CRTM). It is shown that the biases of those temperature channels can reach up to 12 K and are dramatically reduced to below 4 K during daytime by the fast NLTE model with small differences from the nighttime biases. However, the biases after applying the NLTE correction remain large for CrIS upper atmospheric temperature channels during both daytime and nighttime. A further investigation suggests that the remaining biases in those temperature channels mainly originate from the cold biases in the stratospheric temperature profiles of National Center for Environmental Prediction Global Forecast System (GFS) forecasts as input to CRTM. The cold biases reach the maximum of about 8 K near the tropics at about 1 hPa, decreasing toward higher latitudes and lower altitudes.

Multi-model ensemble-based probabilistic prediction of tropical cyclogenesis using TIGGE model forecasts

An extended range tropical cyclogenesis forecast model has been developed using the forecasts of global models available from TIGGE portal. A scheme has been developed to detect the signatures of cyclogenesis in the global model forecast fields [i.e., the mean sea level pressure and surface winds (10 m horizontal winds)]. For this, a wind matching index was determined between the synthetic cyclonic wind fields and the forecast wind fields. The thresholds of 0.4 for wind matching index and 1005 hpa for pressure were determined to detect the cyclonic systems. These detected cyclonic systems in the study region are classified into different cyclone categories based on their intensity (maximum wind speed). The forecasts of up to 15 days from three global models viz., ECMWF, NCEP and UKMO have been used to predict cyclogenesis based on multi-model ensemble approach. The occurrence of cyclonic events of different categories in all the forecast steps in the grided region (10 × 10 km2) was used to estimate the probability of the formation of cyclogenesis. The probability of cyclogenesis was estimated by computing the grid score using the wind matching index by each model and at each forecast step and convolving it with Gaussian filter. The proposed method is used to predict the cyclogenesis of five named tropical cyclones formed during the year 2013 in the north Indian Ocean. The 6–8 days advance cyclogenesis of theses systems were predicted using the above approach. The mean lead prediction time for the cyclogenesis event of the proposed model has been found as 7 days.

A bias corrected WRF mesoscale fire weather dataset for Victoria, Australia 1972-2012

Climatology data of fire weather across the landscape can provide science-based evidence for informing strategic decisions to ameliorate the impacts (at times extreme) of bushfires on community socio-economic wellbeing and to sustain ecosystem health and functions. A long-term climatology requires spatial and temporal data that are consistent to represent the landscape in sufficient detail to be useful for fire weather studies and management purposes. To address this inhomogeneity problem for analyses of a variety of fire weather interests and to provide a dataset for management decision-support, a homogeneous 41-year (1972-2012), hourly interval, 4 km gridded climate dataset for Victoria has been generated using a combination of mesoscale modelling, global reanalysis data, surface observations, and historic observed rainfall analyses. Hourly near-surface forecast fields were combined with Drought Factor (DF) fields calculated from the Australian Water Availability Project (AWAP) rainfall analyses to generate fields of hourly fire danger indices for each hour of the 41-year period. A quantile mapping (QM) bias correction technique utilizing available observations during 1996-2012 was used to ameliorate any model biases in wind speed, temperature and relative humidity. Extensive evaluation was undertaken including both quantitative and case study qualitative assessments. The final dataset includes 4-km surface hourly temperature, relative humidity, wind speed, wind direction, Forest Fire Danger Index (FFDI), and daily DF and Keetch-Byram Drought Index (KBDI), and a 32-level full three-dimensional volume atmosphere.

The evolution of the ECMWF hybrid data assimilation system

The trend towards using flow‐dependent, ensemble‐based estimates of background‐error covariances has been one of the main themes of atmospheric data assimilation research and development in recent years. In this work it is documented how flow‐dependent ensemble information from the ECMWF ensemble of data assimilations (EDA) has gradually been incorporated into the B model which describes the background‐error covariance matrix at the start of the ECMWF 4D‐Var assimilation window. Starting with background‐error variances for the balanced part of the control vector and observation quality control, the current article extends the flow‐dependency to background‐error variances for the unbalanced part of the control vector and for background‐error correlation structures. The correlations are determined either online from previous days or from a hybrid of climatological and current cycle estimates. Each of these changes is shown to improve both the realism of the modelled B and the accuracy of the analysis and forecast fields produced by the 4D‐Var assimilation cycle which makes use of the improved B. Finally, increasing the resolution at which the EDA 4D‐Vars are run is shown to reduce the underestimation of the EDA‐based error estimates.