Tropical systematic and random error energetics based on NCEP (MRF) analysis-forecast system — A barotropic approach

Systematic and random error and their growth rate and different components of growth rate budget in energy/variance form are investigated at wavenumber domain for medium range tropical (30‡S-30‡N) weather forecast using daily horizontal wind field of 850 hPa up to 5-day forecast for the month of June, 2000 of NCEP (MRF) model. Part I of this paper deals with the study at physical domain. The following are the major findings in this paper:

基于实测资料对日蒸散发估算模型的比较

利用设置于江西省南昌县的新型高精度自动蒸渗仪,于2007年9月1日至2008年8月31日的实测陆面实际蒸散发过程,检验了面蒸散发互补关系模型CRAE (Complementary Relationship Areal Evapotranspiration)、GG模型 (Granger-Gray)、平流-干旱模型AA (Advection-Aridity)3个逐日路面实际蒸散发模型在不同时间尺度上的计算精度,并对计算误差的影响因素进行了讨论.结果表明:该地区实测年蒸散发量为746.1 mm,采用各模型的推荐经验参数对该地区蒸散发的估算结果误差较大,普遍干旱条件下蒸散发的计算值比观测值偏小,而湿润条件下的计算值偏大.通过对各模型的经验参数进行调整,各模型对年蒸散发量的计算精度大为提高,但逐日蒸散发过程的计算精度改进效果有限,在7日的时间尺度上,计算结果显著优于逐日的计算结果,在此时间尺度下,AA模型仍存在一定的系统误差,CRAE模型的估算精度相对较差,GG模型的总体计算效果相对最好.根据与蒸渗仪观测结果的对比分析,根据区域特征进行参数调整后的模型,需要在7日及更长时间尺度上,蒸散发模型的估算结果较为可靠.上述研究对全面认识陆面实际蒸散发特征、理解各蒸散发模型在不同时间尺度上的模拟能力、正确认识气候变化条件下的水循环特征具有重要意义.     

Spectral analysis of monsoonal global energy and enstrophy and their nonlinear exchanges in the zonal wavenumber domain

Vertical distributions of various components of the tropospheric global rotational kinetic energy, enstrophy and available potential energy during July 1979, and the contributions to these from different zonal wave categories were studied. Representative levels in the lower and upper troposphere for the stationary and transient energetics were identified on the basis of different components of energy and enstrophy. The eddy energy and enstrophy contained in different zonal scale components in the lower and upper troposphere were studied to find out the preferred scales for stationary and transient monsoonal motion in the two atmospheric layers. The role of different zonal wave categories in the nonlinear exchanges of energy and enstrophy arising due to stationary-stationary, transient-transient, stationary-transient and observed flow interactions was examined. Stationary and transient global spectra of the aforesaid dynamical variables in terms of the zonal wavenumber(m) with triangular truncation atm = 42 were utilized for this purpose. It was found from the global average kinetic energy in lower and upper troposphere that the global stationary and transient motions were comparable in the lower troposphere while in the upper troposphere stationary motion dominated over the transient motion. The computed zonal and eddy energy confirmed that the stationary motion was predominantly zonal while the transient motion was dominated by eddies. From the time mean nonlinear interaction of kinetic energy (enstrophy) of observed flow it was seen that the long and short waves as well as the zonal flow gained kinetic energy (enstrophy) from medium waves due to nonlinear interactions. The transfer of available potential energy due to nonlinear interaction was down the scale except for short waves in the upper troposphere. The stationary-transient interaction was found to be an important element of the spatial-temporal varying atmospheric flow.     

Optimizing the routing of ship’s tropical cyclone avoidance based on the numerical forecasts

In this paper, an algorithm to design a shortest-time route for a ship to avoid a tropical cyclone (TC) is proposed. The proposed algorithm takes into account the influence of the changing winds and sea waves on ship’s speed and the corresponding risk using the forecasts from a numerical weather prediction model. Experimental results show that the new algorithm is able to save more time comparing with the traditional sector diagram typhoon avoidance method. In the application of the new algorithm to the navigation practice, the distance between adjacent alternative waypoints should be adjusted to meet the navigational needs, and the route should be updated simultaneously with TC forecasts from a numerical weather prediction model.     

Multi-Physics ensemble prediction of tropical cyclone movement over Bay of Bengal

Ensemble prediction methodology based on variations in physical process parameterizations in tropical cyclone track prediction has been assessed. Advanced Research Weather Research and Forecasting model with 30-km resolution was used to make 5-day simulation of the movement of Orissa super cyclone (1999), one of the most intense tropical cyclones over the North Indian Ocean. Altogether 36 ensemble members with all possible combinations of three cumulus convection, two planetary boundary layer and six cloud microphysics parameterization schemes were produced. A comparison of individual members indicated that Kain–Fritsch cumulus convection scheme, Mellor–Yamada–Janjic planetary boundary layer scheme and Purdue Lin cloud microphysics scheme showed better performance. The best possible ensemble formulation is identified based on SPREAD and root mean square error (RMSE). While the individual members had track errors ranging from 96–240 km at 24 h to 50–803 km at 120 h, most of the ensemble predictions show significant betterment with mean errors less than 130 km up to 120 h. The convection ensembles had large spread of the cluster, and boundary layer ensembles had significant error disparity, indicating their important roles in the movement of tropical cyclones. Six-member ensemble predictions with cloud microphysics schemes of LIN, WSM5, and WSM6 produce the best predictions with least of RMSE, and large SPREAD indicates the need for inclusion of all possible hydrometeors in the simulation and that six-member ensemble is sufficient to produce the best ensemble prediction of tropical cyclone tracks over Bay of Bengal.     

Evaluation of statistical bias correction methods for numerical weather prediction model forecasts of maximum and minimum temperatures

Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications.     

Assimilation of satellite observations of the atmosphere

Measurements of the atmosphere by satellite were first collected in the 1960s. However, it was not until the mid-1990s that these observations were translated into systematic improvements of numerical weather forecasts. We present here the data and methodology of data assimilation that enabled this achievement. Data assimilation is essentially a filtering processing that exploits the (assumed) known error statistical properties of the observations and of the underlying numerical model in which data are assimilated. It is also a process which corrects the state of the numerical model with physical observations of the real world. This part relies on (assumed) known physical laws to relate meteorological quantities (such as temperature, humidity, pressure, and wind) to observables. Atmospheric data collected by satellite all come from the interaction of electromagnetic waves with the atmosphere. Satellite data assimilation has greatly supported the progress in numerical weather prediction and holds promises for climate studies, for example via reanalysis.     

Intra-seasonal variations of kinetic energy of lower tropospheric zonal waves during northern summer monsoon

Space spectral analysis of zonal (u) and meridional (v) components of wind and time spectral analysis of kinetic energy of zonal waves at 850 hPa during monsoon 1991 (1st June 1991 to 31st August 1991) for the global belt between equator and 40°N are investigated. Space spectral analysis shows that long waves (wavenumbers 1 and 2) dominate the energetics of Region 1 (equator to 20°N) while over Region 2 (20°N to 40°N) the kinetic energy of short waves (wavenumbers 3 to 10) is more than kinetic energy of long waves. It has been found that kinetic energy of long waves is dominated by zonal component while both (zonal and meridional) the components of wind have almost equal contribution in the kinetic energy of short waves. Temporal variations of kinetic energy of wavenumber 2 over Region 1 and Region 2 are almost identical. The correlation matrix of different time series shows that (i) wavenumber 2 over Regions 1 and 2 might have the same energy source and (ii) there is a possibility of an exchange of kinetic energy between wavenumber 1 over Region 1 and short waves over Region 2. Wave to wave interactions indicate that short waves over Region 2 are the common source of kinetic energy to wavenumber 2 over Regions 1 and 2 and wavenumber 1 over Region 1. Time spectral analysis of kinetic energy of zonal waves indicates that wavenumber 1 is dominated by 30–45 day and bi-weekly oscillations while short waves are dominated by weekly and bi-weekly oscillations. The correlation matrix, wave to wave interaction and time spectral analysis together suggest that short period oscillations of kinetic energy of wavenumber 1 might be one of the factors causing dominant weekly (5–9 day) and bi-weekly (10–18 day) oscillations in the kinetic energy of short waves.     

A Study on the Impact of Parameterization of Physical Processes on Prediction of Tropical Cyclones over the Bay of Bengal With NCAR/PSU Mesoscale Model

Prediction of the track and intensity of tropical cyclones is one of the most challenging problems in numerical weather prediction (NWP). The chief objective of this study is to investigate the performance of different cumulus convection and planetary boundary layer (PBL) parameterization schemes in the simulation of tropical cyclones over the Bay of Bengal. For this purpose, two severe cyclonic storms are simulated with two PBL and four convection schemes using non-hydrostatic version of MM5 modeling system. Several important model simulated fields including sea level pressure, horizontal wind and precipitation are compared with the corresponding verification analysis/observation. The track of the cyclones in the simulation and analysis are compared with the best-fit track provided by India Meteorological Department (IMD). The Hong-Pan PBL scheme (as implemented in NCAR Medium Range Forecast (MRF) model) in combination with Grell (or Betts-Miller) cumulus convection scheme is found to perform better than the other combinations of schemes used in this study. Though it is expected that radiative processes may not have pronounced effect in short-range forecasts, an attempt is made to calibrate the model with respect to the two radiation parameterization schemes used in the study. And the results indicate that radiation parameterization has noticeable impact on the simulation of tropical cyclones.     

Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models.     

Equatorial modes observed in atmospheric variables

Wavenumber-frequency spectral analysis of different atmospheric variables has been carried out using 25 years of data. The area considered is the tropical belt 25°S–25°N. A combined FFT-wavelet analysis method has been used for this purpose. Variables considered are outgoing long-wave radiation (OLR), 850 hPa divergence, zonal and meridional winds at 850, 500 and 200 hPa levels, sea level pressure and 850 hPa geopotential height. It is shown that the spectra of different variables have some common properties, but each variable also has few features different from the rest. While Kelvin mode is prominent in OLR and zonal winds, it is not clearly observed in pressure and geopotential height fields; the latter two have a dominant wavenumber zero mode not seen in other variables except in meridional wind at 200 hPa and 850 hPa divergences. Different dominant modes in the tropics show significant variations on sub-seasonal time scales.     

Decadal Prediction Skill of the Global Sea Surface Temperature in the BCC_CSM1.1 Climate Model

This study assesses retrospective decadal prediction skill of Sea Surface Temperature (SST) variability in initialized climate prediction experiments (INT) with the Beijing Climate Center Climate System Model (BCC_CSM1.1). Ensemble forecasts were evaluated using observations, and compared to an ensemble of uninitialized simulations (NoINT). The results show as follows: ①The warming trend of global mean SST simulated by the INT runs is closer to the observation than that in the NoINT runs.②The INT runs show high SST prediction skills over broad regions of tropical Atlantic, western tropical Pacific and tropical Indian Oceans. ③ In the North Pacific and the east-central tropical Pacific Ocean, the prediction skills are very weak, and there are few improvements coming from the initialization in the INT runs. ④ In the southern Indian Ocean, the prediction skills of the INT runs are significantly larger than that of the NoINT runs, with the maximum skill at the 3~6 and 4~7 years lead time. The above-mentioned conclusions are similar to the results of other climate models. However, the prediction skill in the North Atlantic Ocean is much lower than that of other models, especially in the subpolar region. The low skills in the Atlantic Ocean may be attributed to the misrepresentation of the lead-lag relationship between the Atlantic meridional heat transport and the SST in the BCC_CSM1.1.     

Current status of multimodel superensemble and operational NWP forecast of the Indian summer monsoon

In the last thirty years great strides have been made by large-scale operational numerical weather prediction models towards improving skills for the medium range time-scale of 7 days. This paper illustrates the use of these current forecasts towards the construction of a consensus multimodel forecast product called the superensemble. This procedure utilizes 120 of the recent-past forecasts from these models to arrive at the training phase statistics. These statistics are described by roughly 10⁷ weights. Use of these weights provides the possibility for real-time medium range forecasts with the superensemble. We show the recent status of this procedure towards real-time forecasts for the Asian summer monsoon. The member models of our suite include ECMWF, NCEP/EMC, JMA, NOGAPS (US Navy), BMRC, RPN (Canada) and an FSU global spectral forecast model. We show in this paper the skill scores for day 1 through day 6 of forecasts from standard variables such as winds, temperature, 500 hPa geopotential height, sea level pressure and precipitation. In all cases we noted that the superensemble carries a higher skill compared to each of the member models and their ensemble mean. The skill matrices we use include the RMS errors, the anomaly correlations and equitable threat scores. For many of these forecasts the improvements of skill for the superensemble over the best model was found to be quite substantial. This real-time product is being provided to many interested research groups. The FSU multimodel superensemble, in real-time, stands out for providing the least errors among all of the operational large scale models.     

Customization of WRF-ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean

The convection and planetary boundary layer (PBL) processes play significant role in the genesis and intensification of tropical cyclones (TCs). Several convection and PBL parameterization schemes incorporate these processes in the numerical weather prediction models. Therefore, a systematic intercomparison of performance of parameterization schemes is essential to customize a model. In this context, six combinations of physical parameterization schemes (2 PBL Schemes, YSU and MYJ, and 3 convection schemes, KF, BM, and GD) of WRF-ARW model are employed to obtain the optimum combination for the prediction of TCs over North Indian Ocean. Five cyclones are studied for sensitivity experiments and the out-coming combination is tested on real-time prediction of TCs during 2008. The tracks are also compared with those provided by the operational centers like NCEP, ECMWF, UKMO, NCMRWF, and IMD. It is found that the combination of YSU PBL scheme with KF convection scheme (YKF) provides a better prediction of intensity, track, and rainfall consistently. The average RMSE of intensity (13?hPa in CSLP and 11?m?s^?1 in 10-m wind), mean track, and landfall errors is found to be least with YKF combination. The equitable threat score (ETS) of YKF combination is more than 0.2 for the prediction of 24-h accumulated rainfall up to 125?mm. The vertical structural characteristics of cyclone inner core also recommend the YKF combination for Indian seas cyclones. In the real-time prediction of 2008 TCs, the 72-, 48-, and 24-h mean track errors are 172, 129, and 155?km and the mean landfall errors are 125, 73, and 66?km, respectively. Compared with the track of leading operational agencies, the WRF model is competing in 24?h (116?km error) and 72?h (166?km) but superior in 48-h (119?km) track forecast.     

Performance evaluation of HWRF model during post monsoon tropical cyclone Nilofar

An accurate tropical cyclone track and intensity forecast is very important for disaster management. Specialized numerical prediction models have been recently used to provide high-resolution temporal and special forecasts. Hurricane Weather Research and Forecast (HWRF) model is one of the emerging numerical models for tropical cyclone forecasting. This study evaluates the performance of HWRF model during the post monsoon tropical cyclone Nilofar on the north Indian Ocean basin. The evaluation uses the best track data provided by the Indian Meteorological Department (IMD) and the Joint Typhoon Warning Centre (JTWC). Cyclone track, central pressure, and wind speed are covered on this evaluation. Generally, HWRF was able to predict the Nilofar track with track error less than 230 km within the first 66 h of forecast time span. HWRF predicted more intense tropical cyclone. It predicted the lowest central pressure to be 922 hPa while it reached 950 hPa according to IMD and 937 hPa according to JTWC. Wind forecast was better as it predicted maximum wind speed of 122 kt while it reached 110 and 115 kt according to IMD and JTWC, respectively.     

Climate prediction experiences in southern Africa 1990–2005 and key outcomes

This paper reviews long-range climate prediction with a focus on experiences in southern Africa from 1990 to 2005 and statistical techniques based on historical replication. The initial growth and use of climate predictions in southern Africa created an understanding of tropical Atlantic and Indian Ocean coupling with Pacific El Niño and the African monsoon, and fostered extensions of the global observing system as a key outcome. An intercomparison of forecast errors highlights optimal scales for model aggregation. Cases of forecast development are analyzed and a tendency for neutral messages is found. South African maize yield exhibits an uptrend (+0.26 T ha^?1/year) since 1992 that may be related to the increased use of Internet and uptake of long-range forecasts.     

Energetics of lower tropospheric ultra-long waves: A key to intra-seasonal variability of Indian monsoon

Analysis of fifty four (1951–2004) years of daily energetics of zonal waves derived from NCEP/NCAR wind (u and υ) data and daily rainfall received over the Indian landmass (real time data) during southwest monsoon season (1 June–30 September) indicate that energetics (momentum transport and kinetic energy) of lower tropospheric ultra-long waves (waves 1 and 2) of low latitudes hold a key to intra-seasonal variability of monsoon rainfall over India. Correlation coefficient between climatology of daily (122 days) energetics of ultra-long waves and climatology of daily rainfall over Indian landmass is 0.9. The relation is not only significant but also has a predictive potential. The normalised plot of both the series clearly indicates that the response period of rainfall to the energetics is of 5–10 days during the onset phase and 4–7 days during the withdrawal phase of monsoon over India. During the established phase of monsoon, both the series move hand-in-hand. Normalised plot of energetics of ultra-long waves and rainfall for individual year do not show marked deviation with respect to climatology. These results are first of its kind and are useful for the short range forecast of rainfall over India.     

Moist baroclinic instability and the growth of monsoon depressions— linear and nonlinear studies

The objective of this study was two-fold: the first to investigate the role of moist convection and nongeostrophic effects on the growth of the monsoon depressions using a linearized multi-level moist primitive equation (PE) model and quasi-geostrophic (QG) model with only vertical shear. The second was to study the nonlinear evolution, growth, movement and detailed energetics of the monsoon depressions using a nonlinear moist global spectral model. Our linear studies using both models revealed lower as well as upper tropospheric growing modes. For the lower tropospheric modes the shorter scales were found to grow faster. While the PE model showed faster growth rate for shorter scales, as compared to longer scales, the QG model showed less tendency for scale selection. The shorter scales in PE model had phase speeds ranging from 4 to — 1 ms−1 and in QG model from 8 to — 4 ms−1. The nongeostrophic effects were found to be, in general, important. One of the lower tropospheric modes with wavelength 2500 km was found to have many features similar to the observed monsoon depression of the Bay of Bengal. In the upper troposphere the PE model showed much faster growth rates compared to the QG model. Also the fastest growing mode with a doubling time of 2.5 days had a scale of 6000 km. This was shorter than the scale predicted in the QG model. This mode had many characteristics similar to the observed features of the monsoon upper tropospheric easterly waves. Using a nonlinear global spectral model, we simulated the monsoon depression around 21°N starting from an antisymmetric heating distribution (with respect to the equator) and with a specific vertical structure with and without basic flows. The model was integrated for a period of five days incorporating a simple form of cumulus heating. The simulated model disturbance showed a pronounced growth and a westward movement in the presence of cumulus heating. The detailed energetics calculations revealed that the baroclinic energy exchange is the primary energy exchange process and cumulus heating is the driving force for the generation of available potential energy.     

NON-LINEAR WAVE-INDUCED RESPONSE OF POROUS SEABED: A FINITE ELEMENT ANALYSIS

Conventional investigations of waves–seabed interaction problems have been only concerned with the soil response due to two-dimensional linear progressive waves over a uniform seabed. However, the effects of non-linear waves which have been reported in the literature may be significantly different. In this paper, a finite element model is developed to investigate the non-linear wave-induced seabed response with variable permeability and shear modulus in a three-dimensional domain. The finite element formulations are fully presented in this paper. The numerical model is verified with the previous investigations through the reduced form of the present solution. The numerical results indicate that the influence of non-linear wave components cannot always be ignored without substantial error. Furthermore, the wave-induced seabed response is affected significantly by variable permeability in coarser seabeds and variable shear modulus in finer seabeds. © 1997 by John Wiley & Sons, Ltd.