Further experiments on cyclone track prediction with a quasi-Lagrangian limited area model

Summary Results of an earlier study of cyclone track prediction using a quasi-Lagrangian model (QLM) to generate track forecasts of up to 36 hours were reported by Prasad and Rama Rao (2003). Further experiments to produce track forecasts of up to 72 hours with an updated version of the same model have been carried out in the present study. In this case, the ability of the model to predict recent historical cyclones in the Bay of Bengal and Arabian Sea has been assessed. Analysis of some of the structural features of analyzed and predicted fields has been carried out. Such fields include wind distribution and vertical motion around the cyclone centre. In addition, the merging of an idealized vortex with the large scale initial fields provided by a global model, has been carried out for a particular case study of a May 1997 storm, which hit the Bangladesh coast. This current study has demonstrated that the model generates a realistic structure of a tropical cyclone with an idealized vortex. Performance evaluation has been carried out by computing the direct position errors (DPE). The results of which show that the mean error for a 24 h forecast is about 122 km, which increases to about 256 km for a 48 h forecast and 286 km for a 72 h forecast. These figures are comparable to similar errors in respect of tropical cyclone forecasts produced by an advanced NWP centre, viz., the UKMO global model during the corresponding period, 1997–2000 (obtained from UKMO web site). The average forecast errors of the UKMO model are 160 km for 24 h, 265 km for 48 h, 415 km for 72 h forecast ranges.     

Evaluation of long-term trends in tropical cyclone intensity forecasts

Summary The National Hurricane Center and Joint Typhoon Warning Center operational tropical cyclone intensity forecasts for the three major northern hemisphere tropical cyclone basins (Atlantic, eastern North Pacific, and western North Pacific) for the past two decades are examined for long-term trends. Results show that there has been some marginal improvement in the mean absolute error at 24 and 48 h for the Atlantic and at 72 h for the east and west Pacific. A new metric that measures the percent variance of the observed intensity changes that is reduced by the forecast (variance reduction, VR) is defined to help account for inter-annual variability in forecast difficulty. Results show that there have been significant improvements in the VR of the official forecasts in the Atlantic, and some marginal improvement in the other two basins. The VR of the intensity guidance models was also examined. The improvement in the VR is due to the implementation of advanced statistical intensity prediction models and the operational version of the GFDL hurricane model in the mid-1990s. The skill of the operational intensity forecasts for the 5-year period ending in 2005 was determined by comparing the errors to those from simple statistical models with input from climatology and persistence. The intensity forecasts had significant skill out to 96 h in the Atlantic and out to 72 h in the east and west Pacific. The intensity forecasts are also compared to the operational track forecasts. The skill was comparable at 12 h, but the track forecasts were 2 to 5 times more skillful by 72 h. The track and intensity forecast error trends for the two-decade period were also compared. Results showed that the percentage track forecast improvement was almost an order of magnitude larger than that for intensity, indicating that intensity forecasting still has much room for improvement.     

Cyclone track prediction by a quasi-Lagrangian limited area model

Summary. ?Cyclone track predictions in the Indian seas (Bay of Bengal and Arabian Sea) with a quasi-Lagrangian model (QLM) have been attempted. QLM has a horizontal resolution of 40 km and 16 sigma levels in the vertical. It is integrated in a domain of about 4400 × 4400 km². A new initialization procedure to provide initial fields for running the model has been designed. The initialization procedure consists of updating the global model forecasts, used as first guess, provided by the National Center for Medium Range Weather Forecasting (NCMRWF), New Delhi. A new version of IMD’s operational optimum interpolation scheme has been created to suit the QLM grid structure. Lateral boundary conditions are computed from the extended forecasts of NCMRWF. The track forecasts in each case show a reasonable skill of the forecast model in predicting the direction of movement within acceptable limits of forecast errors, which are comparable to some of the best models operated by advanced NWP centers of the world. Even the recurving storms are well predicted. Evolution of the vertical motion fields are also studied which reveal some interesting features, which are described in detail in the text. The composited vertical motion fields are projected against observed rainfall distribution, which show a good spatial correspondence. Received August 9, 2001; revised March 12, 2002; accepted June 17, 2002 Published online: May 8, 2003     

Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe

Soil moisture exhibits outstanding memory characteristics and plays a key role within the climate system. Especially through its impacts on the evapotranspiration of soils and plants, it may influence the land energy balance and therefore surface temperature. These attributes make soil moisture an important variable in the context of weather and climate forecasting. In this study we investigate the value of (initial) soil moisture information for sub-seasonal temperature forecasts. For this purpose we employ a simple water balance model to infer soil moisture from streamflow observations in 400 catchments across Europe. Running this model with forecasted atmospheric forcing, we derive soil moisture forecasts, which we then translate into temperature forecasts using simple linear relationships. The resulting temperature forecasts show skill beyond climatology up to 2 weeks in most of the considered catchments. Even if forecasting skills are rather small at longer lead times with significant skill only in some catchments at lead times of 3 and 4 weeks, this soil moisture-based approach shows local improvements compared to the monthly European Centre for Medium Range Weather Forecasting (ECMWF) temperature forecasts at these lead times. For both products (soil moisture-only forecast and ECMWF forecast), we find comparable or better forecast performance in the case of extreme events, especially at long lead times. Even though a product based on soil moisture information alone is not of practical relevance, our results indicate that soil moisture (memory) is a potentially valuable contributor to temperature forecast skill. Investigating the underlying soil moisture of the ECMWF forecasts we find good agreement with the simple model forecasts, especially at longer lead times. Analyzing the drivers of the temperature forecast skills we find that they are mainly controlled by the strengths of (1) the soil moisture-temperature coupling and (2) the soil moisture memory. We find a negative relationship between these controls that weakens the forecast skills, nevertheless there is a middle ground between both controls in several catchments, as shown by our results.     

Ensemble forecasting of tropical cyclone motion: comparisonbetween regional bred modes and random perturbations

Summary Random perturbations (RPs) and a modified version for breeding of growing modes are used with a regional baroclinic mesoscale model to perform ensemble forecasting of tropical cyclone motion. Based on a sample of six cases, similar conclusions are found as in previous barotropic modeling studies. Even after introducing a larger spatial correlation into the RPs using a multi-quadric analysis scheme, the skill of this ensemble mean track prediction is almost always lower than that of the control forecast in the cases considered. The track prediction performance of the ensemble using regional bred modes (RBMs) as perturbations has a higher average skill. At nearly all forecast intervals except less than 24 h when the initial position error still dominates, the ensemble mean tracks in all six cases are improved over the control forecast. In the 6 h–24 h range, the success rate (ratio of the cases with a forecast improvement to the total number of cases) has a value of 10/24. In the 30 h–48 h range, the success rate increases to 20/24, but drops to 18/24 in the 54 h–72 h range. A relative skill score (RSS) is used to compare the skills of the two perturbation methodologies. It is found that the average RSSs of using RBMs are significantly higher than the corresponding ones of RPs at the 99% confidence level in all three 24-h periods. Note that the above conclusion is only based on ensemble mean forecasts. All of the possibilities from an ensemble-based probabilistic track distribution are not explored in this paper. The ensemble spreads in these RBM ensembles are large enough to include the verifying tracks in all the cases considered. It is also found that the ensemble spread is well correlated with the average error in an ensemble when using RBMs, but not with the ensemble mean forecast error in both methodologies. Received February 7, 2001/Revised April 18, 2001     

Improved tropical cyclone track predictions using error recycling

Summary Errors produced by a nonlinear predictive scheme contain information about both the observations and the prediction system. Therefore, its error history would be expected to contribute to increasing the skill of the predictions if it is included in the forecast. In this study an error recycling procedure is developed for tropical cyclone track prediction. Errors are defined here as differences between the model forecast and the best track position. Error histories are incorporated into a nonlinear analogue, or simplex, forecast scheme and applied to tropical cyclone track prediction, using the archives of observed position data associated with the forecast errors. Various forecast experiments of the cyclone tracks are performed: standard simplex predictions using observed positions only; simplex predictions improved by error forecasts based on libraries of both observations and the recycled forecast errors; and, finally, predictions that include NWP-model forecasts and their errors as predictors. The resulting gains in skill of predictions out to 72 hours ahead are found to be substantial. Received August 12, 1999 Revised November 5, 1999     

Skill as a function of time scale in ensembles of seasonal hindcasts

Forecast skill as a function of time lead and time averaging is examined in two 6-member ensembles of seasonal hindcasts. One ensemble is produced with the second generation general circulation model of the Canadian Centre for Climate Modelling and Analysis (GCM2) and the other with a reduced resolution version of the numerical weather prediction model of the Canadian Meteorological Centre (SEF). The integrations are initiated from the NCEP/NCAR reanalyzed data. Monthly sea surface temperature anomalies observed prior to the forecast period are maintained throughout the forecast season. A statistical forecast improvement technique, based on the singular value decomposition of forecast and reanalyzed fields, is discussed and evaluated. A simple analogue of the hindcast integrations is used to examine the behavior of two common skill scores, the correlation skill score and the explained variance skill score. The maximal skill score and the corresponding optimal forecast in this analogue are identified. The total skill of the optimal forecast is a sum of two terms, one associated with the initial conditions and the other with the lower boundary forcing. The two sources of skill operate on different time scales, with initial conditions being more important in the first one-two weeks and the atmospheric response to the boundary forcing becoming more dominant for longer time leads and time averages. This suggests that these sources of skill should be considered separately in forecast optimization. The statistical technique is moderately successful in improving the skill of monthly to seasonal forecasts of 500 hPa height (Z ₅₀₀) and 700 hPa temperature (T ₇₀₀) in the Northern Hemisphere and in the North Pacific/North America sector. The improvement is better when the forecasts for the first week and for the rest of the season are optimized separately. The SEF model produces better Z ₅₀₀ and T ₇₀₀ forecasts than GCM2 in the first one-two weeks whereas GCM2 performs slightly better at longer time leads. The skill of zero time lead forecast decays rapidly with averaging interval for time averages up to about 30–45 days and stabilizes, or even rises, for longer time averages. Excluding the first week from seasonal forecasts results in substantial degradation of predictive skill. Received: 1 November 1999 / Accepted: 24 May 2000     

Improving Precipitation Forecasts over the Global Tropical Belt

Summary  This study explores the nowcasting and short-range forecasting (up to 3 days) skills of rainfall over the tropics using a high resolution global model. Since the model-predicted rainfall is very sensitive to model parameters, four key model parameters were first selected. They are the Asselin filter coefficient, the fourth order horizontal diffusion coefficient, the surface moisture flux coefficient, and the vertical diffusion coefficient. The optimal values were defined as those which contributed to the best one day rainfall forecasts in the present study. In order to demonstrate and improve the precipitation forecast skill, several numerical experiments were designed using the 14-level Florida State University Global Spectral Model (FSUGSM) at a resolution of T106. Comparisons were also made of the short-range forecasts obtained from a control experiment subjected to normal mode initialization (NMI) versus experiments based on physical initialization (PI). The latter experiments were integrated using the original FSUGSM and a modified version. This modified FSUGSM was developed here by applying a reverse cumulus parameterization alorithm to the regular forecast model, which restructures the vertical humidity distribution and constrains the large-scale model’s moisture error growth during the model integration. An improved short-range rainfall prediction skill was achieved from the modified FSUGSM in this study. The results showed a better agreement between model-based and observed rainfall intensity and pattern. Received January 18, 1999     

LLM – a nonhydrostatic model applied to high-resolving simulations of turbulent fluxes over heterogeneous terrain

Summary ?At the Deutscher Wetterdienst (DWD) an internal project named LITFASS was running to determine the representative turbulent fluxes of heat and momentum over heterogeneous land surfaces by observation and simulation. The project took advantage of the infrastructure of the Research Division at the DWD, where model research capacity is combined with the measurements made at and around the Meteorological Observatory Lindenberg. The paper describes the simulation component of the LITFASS-project. It consists of a high-resolving model, derived from the new operational non-hydrostatic, compressible Lokal-Modell (LM), which is denoted LLM (LITFASS-Lokal-Modell). The integration area covers the lower atmosphere in the vertical up to 3000 m with 39 model layers. The horizontal size of the integration area with 145 × 145 grid points (horizontal mesh width Δs = 96.5 m) corresponds to a typical grid box of a meso-scale model. The LLM has to operate under real meteorological conditions. Therefore, the LLM is driven by time-dependent measured vertical profiles of wind, temperature and humidity and surface-based measurements (of radiation, precipitation, soil properties) supported by satellite information. The profiles are available for a great variety of weather situations occurring during the simulation period (1–20 June 1998). First model results from extended 24 hour-integrations against different kinds of measurements are discussed. They reveal the LLM to become a promising validation instrument, from which a systematic, sustainable validation system can be established beyond LITFASS for improving parameterization schemes in the NWP models of the DWD. Received July 18, 2001; revised March 15, 2002; accepted May 30, 2002     

Simulation of maximum surface air temperature over India using the UK Met Office Global Seasonal (GloSea) Model

Summary A comparative study was performed to evaluate the performance of the UK Met Office’s Global Seasonal (GloSea) prediction General Circulation Model (GCM) for the forecast of maximum surface air temperature (T_max) over the Indian region using the model generated hindcast of 15-members ensemble for 16 years (1987–2002). Each hindcast starts from 1^st January and extends for a period of six months in each year. The model hindcast T_max is compared with T_max obtained from verification analysis during the hot weather season on monthly and seasonal scales from March to June. The monthly and seasonal model hindcast climatology of T_max from 240 members during March to June and the corresponding observed climatology show highly significant (above 99.9% level) correlation coefficients (CC) although the hindcast T_max is over-estimated (warm bias) over most parts of the Indian region. At the station level over New Delhi, although the forecast error (forecast-observed) at the monthly scale gradually increases from March to June, the forecast error at the seasonal scale during March to May (MAM) is found to be just 1.67 °C. The GloSea model also simulates well T_max anomalies on monthly and seasonal scales during March to June with the lower Root Mean Square Error (RMSE) of bias corrected forecast (less than 1.2 °C), which is much less than the corresponding RMSE of climatology (reference) forecast. The anomaly CCs (ACCs) over the station in New Delhi are also highly significant (above 95% level) on monthly to seasonal time scales from March to June, except for April. The skill of the GloSea model for the seasonal forecast of T_max as measured from the ACC map and the bias corrected RMSE map is reasonably good during MAM and April to June (AMJ) with higher ACC (significant at 95% level) and lower RMSE (less than 1.5 °C) found over many parts of the Indian regions. Authors’ addresses: D. R. Pattanaik, H. R. Hatwar, G. Srinivasan, Y. V. Ramarao, India Meteorological Department (IMD), New Delhi, India; U. C. Mohanty, P. Sinha, Centre for Atmospheric Sciences, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India; Anca Brookshaw, UK Met Office, UK.     

Adaptive observations for hurricane prediction

Summary This study proposes a method that can be used to provide guidelines to aircraft reconnaissance for hurricane observations. The method combines numerical weather prediction (NWP) model with a statistical approach to target adaptive observations over areas where the hurricane predictions are very sensitive to the initial analysis for the NWP-model. A single model experiment is performed using regular initial analysis, while 50 other ensemble runs are performed from randomly perturbed initial states. Under the perfect model assumption, the single model experiment serves as a true state. The method first computes the forecast error variances at a certain verification time, e.g. hour 48, and then locates the maximum centers of variances. After the locations of the maximum forecast error variances are known, various correlations of different variables between these maximum variance points and the perturbation fields at the target time, e.g. hour 12, are calculated to identify those locations at the target time, over where the observational errors might be responsible for the growth of forecast error variances at the verification time. Statistically, these correlation fields indicate where the most sensitive areas are at the target time, i.e. where the need for additional observations is suggested. Hurricane Fran of 1996 is used to test the proposed method. The reason for choosing this case is that, during the first 48 hour forecast, the track forecast from NWP-model was very close to the best track. Two additional experiments were designed to examine the method. One experiment updates predicted variables at the target time (12 h) over the areas, to where the proposed method indicates the forecast would be sensitive. The updating combines observations (or truth) with the first guess (predicted) fields. Another experiment also modifies predicted variables at the target time (12 h), but over the areas where the method indicates the forecast errors are less correlated to. The results show that the modification has greatly reduced the forecast error variances at the verification time (48 h) in the first experiment, however it has a very little impact on the variance fields at the forecast hour (48 h) in the second experiment. It is very clear from our experiments, that the proposed method is able to identify sensitive areas, where additional observations can help to reduce hurricane forecast errors from an NWP-model. Received July 19, 1999 Revised November 28, 1999     

Skill of seasonal hindcasts as a function of the ensemble size

 Forecast skill as a function of the ensemble size is examined in a 24-member ensemble of northern winter (DJF) hindcasts produced with the second generation general circulation model of the Canadian Centre for Climate Modelling and Analysis. These integrations are initialized from the NCEP reanalyses at 6 h intervals prior to the forecast season. The sea surface temperatures that are applied as lower boundary conditions are predicted by persisting the monthly mean anomaly observed prior to the forecast period. The potential predictability that is attributed to lower boundary forced variability is estimated. In lagged-average forecasting, the forecast skill in the first two weeks, which originates predominately from the initial conditions, is greatest for relatively small ensemble sizes. The forecast skill increases monotonically with the ensemble size in the rest of the season. The skill of DJF 500 hPa geopotential height hindcasts in the Northern Hemisphere and in the Pacific/North America sector improves substantially when the ensemble size increases from 6 to 24. A statistical skill improvement technique based on the singular value decomposition method is also more successful for larger ensembles. Received: 22 February 2000 / Accepted: 6 December 2000     

Fine-Scale Observations of Summertime Precipitation in an Intra-Alpine Region

Summary ?Intra-mountain summertime precipitation was studied in the Alps in a 40×20 km² area centered around Innsbruck, Austria, from June through September 1997. An observational network with a mean separation distance of 9 km and forecasts from the ECMWF model were used to examine the role the strong forcing from the lower boundary plays in creating “hot spots” for the formation of thunderstorms and the location of heavy precipitation as well as systematic precipitation patterns for different weather situations, which can be used to downscale forecasts from global scale routine numerical weather prediction models. Received March 16, 1999/Revised August 20, 1999     

Simulation of heavy precipitation over Santacruz, Mumbai on 26 July 2005, using Mesoscale model

Summary An attempt has been made to simulate the unprecedented heavy precipitation of 94.4 cm in a day over Santacruz, Mumbai during 0300 UTC 26 July to 0300 UTC 27 July 2005. Three experiments have been conducted using Advanced Regional Prediction System model developed by Center for Analysis and Prediction of Storms of Oklahoma University, USA. In first experiment the model input at large domain size has been obtained using NCEP/NCAR reanalysis data at 2.5° × 2.5° lat.–lon. resolution. In other two experiments model input at large as well as at small domain sizes, have been obtained from NCEP/NCAR FNL data of 1° × 1° lat.–lon. resolution. In all three experiments model’s horizontal resolution is 40 km and integration period is 30 hours from 0000 UTC 26 July 2005. Based on the temporal distribution of observed rainfall rates it is considered that the rainfall of 38.1 cm during 0900–1200 UTC on 26 July could be due to cloud burst phenomenon and 56.3 cm from 1200 UTC of 26 July to 0300 UTC of 27 July has been due to continuous regeneration of thunderstorm activity under influence of mesoscale cloud complex. It is found that model forecast of rainfall in first experiment was qualitatively as well as quantitatively very poor. Among other two, experiment with large domain size has predicted better rainfall values and location compared to the experiment with small domain size. The larger domain has produced rainfall of 41 cm as against observed rain rate of 56.3 cm. during 1200 UTC of 26 July to 0300 UTC of 27 July. Divergence, vorticity, vertical velocity and moisture parameters are examined in relation with the various stages of the event. The maximum values of convergence, vorticity and moisture fluxes precede the initial phase of mature stage, however vertical velocity follows the later phase of mature stage. Vorticity budget over the location of maximum rainfall, revealed the significant role of tilting term in maintenance and dissipation of the cloud complex responsible for the event. The model has simulated mixing ratios of ice, snow and hail up to height of 15 km which matches with the observations that clouds reaching up to 15 km were present at the time of event of heavy precipitation.     

Influence of convective parameterization on the systematic errors of Climate Forecast System (CFS) model over the Indian monsoon region from an extended range forecast perspective

This study investigates the influence of Simplified Arakawa Schubert (SAS) and Relax Arakawa Schubert (RAS) cumulus parameterization schemes on coupled Climate Forecast System version.1 (CFS-1, T62L64) retrospective forecasts over Indian monsoon region from an extended range forecast perspective. The forecast data sets comprise 45 days of model integrations based on 31 different initial conditions at pentad intervals starting from 1 May to 28 September for the years 2001 to 2007. It is found that mean climatological features of Indian summer monsoon months (JJAS) are reasonably simulated by both the versions (i.e. SAS and RAS) of the model; however strong cross equatorial flow and excess stratiform rainfall are noted in RAS compared to SAS. Both the versions of the model overestimated apparent heat source and moisture sink compared to NCEP/NCAR reanalysis. The prognosis evaluation of daily forecast climatology reveals robust systematic warming (moistening) in RAS and cooling (drying) biases in SAS particularly at the middle and upper troposphere of the model respectively. Using error energy/variance and root mean square error methodology it is also established that major contribution to the model total error is coming from the systematic component of the model error. It is also found that the forecast error growth of temperature in RAS is less than that of SAS; however, the scenario is reversed for moisture errors, although the difference of moisture errors between these two forecasts is not very large compared to that of temperature errors. Broadly, it is found that both the versions of the model are underestimating (overestimating) the rainfall area and amount over the Indian land region (and neighborhood oceanic region). The rainfall forecast results at pentad interval exhibited that, SAS and RAS have good prediction skills over the Indian monsoon core zone and Arabian Sea. There is less excess rainfall particularly over oceanic region in RAS up to 30 days of forecast duration compared to SAS. It is also evident that systematic errors in the coverage area of excess rainfall over the eastern foothills of the Himalayas remains unchanged irrespective of cumulus parameterization and initial conditions. It is revealed that due to stronger moisture transport in RAS there is a robust amplification of moist static energy facilitating intense convective instability within the model and boosting the moisture supply from surface to the upper levels through convergence. Concurrently, moisture detrainment from cloud to environment at multiple levels from the spectrum of clouds in the RAS, leads to a large accumulation of moisture in the middle and upper troposphere of the model. This abundant moisture leads to large scale condensational heating through a simple cloud microphysics scheme. This intense upper level heating contributes to the warm bias and considerably increases in stratiform rainfall in RAS compared to SAS. In a nutshell, concerted and sustained support of moisture supply from the bottom as well as from the top in RAS is the crucial factor for having a warm temperature bias in RAS.     

基于自动观测站的鲁中地区土壤水分变化规律及精细化预报模型的研究

为了建立鲁中地区土壤水分精细化预报模型,利用2010—2013年农田土壤水分自动站逐日资料进行土壤水分年、月变化特征研究,并结合附近自动气象站资料,以土壤水分平衡方程、农田蒸散模型为基础,采用逐步回归和曲线估计等方法建立4—6月无降水条件下平原水浇田与山旱田土壤水分1 d、7 d降幅的经验预报模型。结果表明:鲁中地区0~100 cm土壤水分贮存量年变化趋势和0~50 cm基本一致,年最高出现在8月,最低出现在6月,年降幅最大出现在3—6月,易出现干旱。对预报模型进行回代和预报检验结果显示,回代平均相对误差为0.07%,7 d模型和1 d模型滚动预报第7天0~50 cm土壤水分贮存量,绝对误差分别为-0.15和-2.17 mm,平均相对误差分别为-0.07%和-1.56%,模型具有较强的理论基础和实用性,预报精度较高,为鲁中地区土壤墒情监测和精细化预报提供支持。     

The transport of momentum, sensible heat, potential energy and moisture over the western Himalayas during the winter season

Summary The western Himalayas receive higher precipitation than the eastern Himalayas during the winter season (December–March). This differential pattern of winter precipitation over the Himalayas can be attributed to topography and to a higher frequency of disturbances over the western Himalayas, which result in variations in the circulation features. These circulation features, in turn, result in variations in the meridional transport of heat, momentum, potential energy, and moisture across the Himalayas due to mean and eddy motion. Significant meridional transport due to mean motion takes place in the upper troposphere at 300 hPa and 200 hPa. Transport east of 100° E dominates the transport over the western Himalayas. The eddy transport of heat, momentum, and potential energy is considerably smaller than that due to mean motion. Eddy transport magnitudes are smaller up to 500 hPa and increase rapidly aloft to 300 hPa and 200 hPa. Eddy transport over the western Himalayas is greater than over the eastern Himalayas.     

Soil moisture effects on seasonal temperature and precipitation forecast scores in Europe

The Second Global Land Atmosphere Coupling Experiment (GLACE2) is designed to explore the improvement of forecast skill of summertime temperature and precipitation up to 8?weeks ahead by using realistic soil moisture initialization. For the European continent, we show in this study that for temperature the skill does indeed increase up to 6 weeks, but areas with (statistically significant) lower skill also exist at longer lead times. The skill improvement is smaller than shown earlier for the US, partly because of a lower potential predictability of the European climate at seasonal time scales. Selection of extreme soil moisture conditions or a subset of models with similar initial soil moisture conditions does improve the forecast skill, and sporadic positive effects are also demonstrated for precipitation. Using realistic initial soil moisture data increases the interannual variability of temperature compared to the control simulations in the South-Central European area at longer lead times. This leads to better temperature forecasts in a remote area in Western Europe. However, the covered range of forecast dates (1986–1995) is too short to isolate a clear physical mechanism for this remote correlation.     

Characteristics of the water cycle and land–atmosphere interactions from a comprehensive reforecast and reanalysis data set: CFSv2

The behavior of the water cycle in the Coupled Forecast System version 2 reforecasts and reanalysis is examined. Attention is focused on the evolution of forecast biases as the lead-time changes, and how the lead-time dependent model climatology differs from the reanalysis. Precipitation biases are evident in both reanalysis and reforecasts, while biases in soil moisture grow throughout the duration of the forecasts. Locally, the soil moisture biases may shrink or reverse sign. These biases are reflected in evaporation and runoff. The Noah land surface scheme shows the necessary relationships between evaporation and soil moisture for land-driven climate predictability. There is evidence that the atmospheric model cannot maintain the link between precipitation and antecedent soil moisture as strongly as in the real atmosphere, potentially hampering prediction skill, although there is better precipitation forecast skill over most locations when initial soil moisture anomalies are large. Bias change with lead-time, measured as the variance across ten monthly forecast leads, is often comparable to or larger than the interannual variance. Skill scores when forecast anomalies are calculated relative to reanalysis are seriously reduced over most locations when compared to validation against anomalies based on the forecast model climate at the corresponding lead-time. When all anomalies are calculated relative to the 0-month forecast, some skill is recovered over some regions, but the complex manner in which biases evolve indicates that a complete suite of reforecasts would be necessary whenever a new version of a climate model is implemented. The utility of reforecast programs is evident for operational forecast systems.     

The surface energy balance of a snow cover: comparing measurements to two differentsimulation models

Summary  We compared two one-dimensional simulation models for heat and water fluxes in the soil-snow-atmosphere system with respect to their mathematical formulations of the surface heat exchange and the snow pack evolution. They were chosen as examples of a simple one-layer snow model and a more detailed multiple-layer snow model (SNTHERM). The snow models were combined with the same one-dimensional model for the heat and water balance of the underlying soil (CoupModel). Data from an arable field in central Sweden (Marsta), covering two years (1997–1999) of soil temperature, snow depth and eddy-correlation measurements were successfully compared with the models. Conditions with a snow pack deeper or shallower than 10 cm and bare soil resulted in similar discrepancies. The simulated net radiation and sensible heat flux were in good agreement with that measured during snow-covered periods, except for situations with snowmelt when the downward sensible heat flux was overestimated by 10–20 Wm⁻². The results showed that the uncertainties in parameter values were more important than the model formulation and that both models were useful in evaluating the limitations and uncertainties of the measurements. Received November 1, 1999 Revised April 20, 2000