Low Visibility Formation and Forecasting on the Northern Coast of Brazil

Visibility analysis and forecast at the Maceio International Airport in the Brazilian Northeast (NEB) was the principal goal of this investigation. Surface meteorological data of the Maceio International Airport were used for low visibility frequency study. Low visibility in NEB was provoked more frequently by light fog (LF) formation (1,098 or 92 h month^?1 on average). Haze and fog were very rare (81 h and one event per year, respectively on average). Light fog with a visibility less than 2 km usually was detected together with rain or drizzle. Low visibility was observed more frequently at night and during the rainy season. Applications of the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model for light fog forecast were tested. Thermodynamic processes were studied by vertical profile, elaborated by: (1) National Centers for Environmental Prediction (NCEP) reanalysis data for Maceio (because of some radiosonde absence) and (2) forecast vertical temperature and humidity profiles were produced, using Air Parcels Trajectories of the HYSPLIT model at the pattern levels. The synoptic situations before and during low visibility phenomena were analyzed using different products of NCEP reanalysis, the high resolution (10 km) ETA model and infrared satellite images. Wave disturbance in the trade winds field, localized on the northwest periphery of the South Atlantic subtropical High, usually accompanied the phenomena. A humidity advection, weak ascendant movement and thermal inversion absence at the low levels were created by these waves. The middle level’s descendent movement provoked the humidity accumulation at levels below. Satisfactory results of the HYSPLIT model applications for light fog forecast were obtained with 12 h antecedence. In particular, stable level forecast by the ETA model was forecast satisfactorily with 12 h antecedence; vertical movements were predicted better with up to 48 h antecedence. The PSU/NCAR mesoscale model (MM5) and PAFOG models were tested for analysis and forecast of an intensive fog event. Intensive fog provoked a fatal accident of a small airplane near the Maceio Airport in 2007. These fog formation processes were studied by NCEP reanalysis data, the high resolution regional model MM5, and satellite and radar data. Fog formation was simulated by PAFOG model and satisfactory results were obtained with 10 h antecedence.     

基于遗传算法优化的ENSO指数的动力预报模型反演

基于NCEP/NCAR提供的1958～1995年全球月平均海温距平场再分析资料,采用动力系统反演思想和遗传算法途径,进行了El Nino/La Nina指数的动力预报模型的参数优化和模型反演,从上述海温资料中重构了Nino3海温距平指数的非线性动力模型.模型预报试验结果表明,遗传算法具有的全局搜索和并行计算优势能够客观、有效地反演海温指数的动力预报模型,对Nino3海温指数和El Nino/La Nina事件进行较为客观准确的预测,为El Nino/La Nina预测提供有益的研究参考.     

A land surface soil moisture data assimilation framework in consideration of the model subgrid-scale heterogeneity and soil water thawing and freezing

The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent.     

Predictability of storm wave heights in the ice-free Beaufort Sea

A predictability study on wave forecast of the Arctic Ocean is necessary to help identify hazardous areas and ensure sustainable shipping along the trans-Arctic routes. To assist with validation of the Arctic Ocean wave model, two drifting wave buoys were deployed off Point Barrow, Alaska for two months in September 2016. Both buoys measured significant wave heights exceeding 4 m during two different storm events on 19 September and 22 October. The NOAA-WAVEWATCH III^? model with 16-km resolution was forced using wind and sea ice reanalysis data and obtained general agreement with the observation. The September storm was reproduced well; however, model accuracy deteriorated in October with a negative wave height bias of around 1 m during the October storm. Utilising reanalysis data, including the most up-to-date ERA5, this study investigated the cause: grid resolution, wind and ice forcing, and in situ sea level pressure observations assimilated for reanalysis. The analysis has found that there is a 20% reduction of in situ SLP observations in the area of interest, presumably due to fewer ships and deployment options during the sea ice advance period. The 63-member atmospheric ensemble reanalysis, ALERA2, has shown that this led to a larger ensemble spread in the October monthly mean wind field compared to September. Since atmospheric physics is complex during sea ice advance, it is speculated that the elevated uncertainty of synoptic-scale wind caused the negative wave model bias. This has implications for wave hindcasts and forecasts in the Arctic Ocean.     

中国区域夏季再分析资料高空变量可信度的检验

利用全球探空资料(IGRA)对1989—2008年美国国家环境预报中心(NCEP)和大气研究中心(NCAR)再分析资料、NCEP和美国能源部(DOE)再分析资料、NCEP气候预测系统再分析资料(CFSR)、日本气象厅25年再分析资料(JRA-25)、欧洲数值预报中心再分析资料(ERA-Interim)和美国国家航空航天局(NASA)现代回顾性再分析资料(MERRA)的高空变量在中国地区对流层中高层的可信度进行了初步的检验.分析结果表明:再分析资料对中高层位势高度和温度的夏季平均气候态具有较好的再现能力,其EOF的时空变化特征与观测吻合也较好;再分析资料的绝对湿度值较观测结果要偏大,其中MERRA与观测最为接近.再分析资料不能很好地反映经向风的夏季平均气候态及年际变化特征,EOF的时空模态和观测偏离也较大.总体而言,NCEP/NCAR、NCEP/DOE及NCEP/CFSR对这些变量的再现能力较JRA-25、ERA-Interim和MERRA弱.     

Application of data assimilation with the Root Zone Water Quality Model for soil moisture profile estimation in the upper Cedar Creek,Indiana

Data assimilation techniques have been proven as an effective tool to improve model forecasts by combining information about observed variables in many areas. This article examines the potential of assimilating surface soil moisture observations into a field‐scale hydrological model, the Root Zone Water Quality Model, to improve soil moisture estimation. The Ensemble Kalman Filter (EnKF), a popular data assimilation technique for nonlinear systems, was applied and compared with a simple direct insertion method. In situ soil moisture data at four different depths (5, 20, 40, and 60 cm) from two agricultural fields (AS1 and AS2) in northeastern Indiana were used for assimilation and validation purposes. Through daily update, the EnKF improved soil moisture estimation compared with the direct insertion method and model results without assimilation, having more distinct improvement at the 5 and 20 cm depths than for deeper layers (40 and 60 cm). Local vertical soil property heterogeneity in AS1 deteriorated soil moisture estimates with the EnKF. Removal of systematic bias in the forecast model was found to be critical for more successful soil moisture data assimilation studies. This study also demonstrates that a more frequent update generally contributes in enhancing the open loop simulation; however, large forecasting error can prevent more frequent update from providing better results. In addition, results indicate that various ensemble sizes make little difference in the assimilation results. An ensemble of 100 members produced results that were comparable with results obtained from larger ensembles. Copyright © 2011 John Wiley & Sons, Ltd.     

Potential and visible evaporation and its variations in European Russia over the recent 50 years by experimental data

Variations in pan evaporation in European Russia from 1951 to 2010 have been studied, and regions with specific variations of potential evaporation have been identified. It is shown that evaporation decseases all over the territory under consideration, and intensity of its decreasing up to the late 1970s was far in excess of that in the decades that followed. The decrease in the variations in evaporation may be regarded as an indicator of reduction of intensity of heat and moisture exchange between the underlying surface and the atmosphere. A new characteristic of the moisture regime of the territory, i.e., visible evaporation, was introduced to characterize, in this case, the amount of free moisture in the atmosphere that can be involved in the terrestrial water cycle. The humidity of the territory in the European Russia has shown to have increased since 1966. Regions where changes in the moisture regime show common patterns have been identified and the specific features of humidity distribution in different natural zones of European Russia have been assessed.     

Impact of ATOVS Radiance on the Analysis and Forecasts of a Mesoscale Model over the Indian Region During the 2008 Summer Monsoon

Assimilation experiments are performed with the Weather Research and Forecasting (WRF) models’ three-dimensional variational data assimilation (3D-Var) scheme to evaluate the impact of directly assimilating the Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) radiance, including AMSU-A, AMSU-B and HIRS, on the analysis and forecasts of a mesoscale model over the Indian region. The present study is, to our knowledge, the first where the impact of ATOVS radiance has been evaluated on the analysis and forecasts of a mesoscale model over the Indian region. The control (without ATOVS radiance) as well as experimental (which assimilated ATOVS radiance) run were made for 48 h starting at 0000 UTC during the entire July 2008. The impacts of assimilating the radiances from different instruments (e.g., AMSU-A, AMSU-B and HIRS) were measured in comparison to the control run. The assimilation experiments for July 2008 (30 cases) demonstrated a positive impact of the assimilated ATOVS radiance on both the analysis state as well as subsequent short-range forecasts. Relative to the control run, the moisture analysis was improved with the assimilation of AMSU-B and HIRS radiance, while AMSU-A was mainly responsible for improved temperature analysis. The comparison of the model-predicted temperature, moisture and wind with NCEP analysis indicated that a positive forecast impact is achieved from each of the three instruments. HIRS and AMSU-A radiance yielded only a slight positive forecast impact, while AMSU-B radiance had the largest positive forecast impact for moisture, temperature and wind. The comparison of model-predicted rainfall with observed rainfall indicates that ATOVS radiance, particularly AMSU-B and HIRS, impacted the rainfall positively. This study clearly shows that the improved analysis of mid-tropospheric moisture, due to the assimilation of AMSU-B radiances, is a key factor to improve the short-term forecast skill of a mesoscale model.     

Integration of Local Observations into the One Dimensional Fog Model PAFOG

The numerical prediction of fog requires a very high vertical resolution of the atmosphere. Owing to a prohibitive computational effort of high resolution three dimensional models, operational fog forecast is usually done by means of one dimensional fog models. An important condition for a successful fog forecast with one dimensional models consists of the proper integration of observational data into the numerical simulations. The goal of the present study is to introduce new methods for the consideration of these data in the one dimensional radiation fog model PAFOG. First, it will be shown how PAFOG may be initialized with observed visibilities. Second, a nudging scheme will be presented for the inclusion of measured temperature and humidity profiles in the PAFOG simulations. The new features of PAFOG have been tested by comparing the model results with observations of the German Meteorological Service. A case study will be presented that reveals the importance of including local observations in the model calculations. Numerical results obtained with the modified PAFOG model show a distinct improvement of fog forecasts regarding the times of fog formation, dissipation as well as the vertical extent of the investigated fog events. However, model results also reveal that a further improvement of PAFOG might be possible if several empirical model parameters are optimized. This tuning can only be realized by comprehensive comparisons of model simulations with corresponding fog observations.     

Prediction and predictability of a catastrophic local extreme precipitation event through cloud-resolving ensemble analysis and forecasting with Doppler radar observations

Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter (EnKF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with EnKF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from EnKF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of EnKF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations.     

Improved SAL method and its application to verifying regional soil moisture forecasting

The regional verification of soil moisture is a vital step in evaluating and improving numerical model performance and utilizing forecast results. Currently, even with improved spatial and temporal resolutions of numerical model, verification methods for soil moisture data still rely on the traditional intensity verification parameters, such as mean error (ME) and root-mean-squared error (RMSE). Those methods provide only incomplete and sometimes inaccurate messages and thus hinder a proper evaluation of a forecast model. The SAL method is an object-based regional verification method with respect to precipitation forecasts. Based on the SAL method, a novel object-based method (SAL-DN) is proposed here, which can be used to test regional soil moisture. Both the ideal experiment and real experiment show that the SAL-DN method can reveal the differences between the observed and forecast soil moisture in three aspects: structure, amplitude, and location, and the results can reflect the actual situation. Furthermore, compared with the SAL method, the SAL-DN method is also capable of verifying physical quantities with high-value and low-value centers like temperature. Therefore, the SAL-DN method enhances verification accuracy and can be applied widely.     

Northern Dvina runoff simulation using land-surface model SWAP and global databases

The potentialities of a method for evaluating runoff from Northern Dvina basin, which is based on a model of heat and water exchange between land surface and the atmosphere (SWAP) in combination with input data based on global databases on land surface parameters and different variants of meteorological data (derived from reanalysis data; reanalysis data hybridized with ground based and satellite observations; observational data of meteorological stations situated in the river basin). In all three cases, an optimization was applied to some key model parameters, including the characteristics of the land surface and correction factors for precipitation and incoming radiation.     

A Study on the Performance of the NCMRWF Analysis and Forecasting System During Asian Summer Monsoon: Thermodynamic Aspects

—The thermodynamic characteristics of the Asian summer monsoon are examined with a global analysis-forecast system. In this study, we investigated the large-scale balances of heat and moisture by making use of operational analyses as well as forecast fields for June, July and August (JJA), 1994. Apart from elucidating systematic errors in the temperature and moisture fields, the study expounds the influence of these errors on the large-scale budgets of heat and moisture over the monsoon region. The temperature forecasts of the model delineate predominant cooling in the middle and lower tropospheres over the monsoon region. Similarly, the moisture forecasts evince a drying tendency in the lower troposphere. However, certain sectors of moderate moistening exist over the peninsular India and adjoining oceanic sectors of the Arabian Sea and Bay of Bengal.¶The broad features of the large-scale heat and moisture budgets represented by the analysis/forecast fields indicate good agreement with the observed aspects of the summer monsoon circulation. The model forecasts fail to retain the analyzed atmospheric variability in terms of the mean circulation, which is indicated by underestimation of various terms of heat and moisture budgets with an increase in the forecast period. Further, the forecasts depict an anomalous diabatic cooling layer in the lower middle troposphere of the monsoon region which inhibits vertical transfer of heat and moisture from the mixed layer of the atmospheric boundary layer to the middle troposphere. In effect, the monsoon circulation is considerably weakened with an increase in the forecast period. The treatment of shallow convection and the use of interactive clouds in the model can reduce the cooling bias considerably.     

Evaluation of the impact of rainfall on soil moisture variability

The impact of rainfall on the spatial-temporal soil moisture variability is investigated by using a model of the soil moisture dynamics and two rainfall models, the noise-forced diffusive precipitation model and the WGR model. The study shows that the variability of the soil moisture field is impacted during the limited time of the storm period. During the interstorm period, the variability of the soil moisture field is closely related with the soil texture, as supported by the analysis of the Washita '92 data set. As the impact of rainfall on the variability of the soil moisture field is limited to the short time period of precipitation, the role of the rainfall is simplified as a source of water to the soil moisture field without any consideration of its variability and/or organization in space. A simulation study of the soil moisture field temporal evolution also supports this result, i.e. a strong relationship between the soil moisture field and the variability of its medium. Also, larger variabilities of the loss field coefficient result in easier removal of moisture from the soil.     

Development and testing of a coupled ocean–atmosphere mesoscale ensemble prediction system

A coupled ocean–atmosphere mesoscale ensemble prediction system has been developed by the Naval Research Laboratory. This paper describes the components and implementation of the system and presents baseline results from coupled ensemble simulations for two tropical cyclones. The system is designed to take into account major sources of uncertainty in: (1) non-deterministic dynamics, (2) model error, and (3) initial states. The purpose of the system is to provide mesoscale ensemble forecasts for use in probabilistic products, such as reliability and frequency of occurrence, and in risk management applications. The system components include COAMPS® (Coupled Ocean/Atmosphere Mesoscale Prediction System) and NCOM (Navy Coastal Ocean Model) for atmosphere and ocean forecasting and NAVDAS (NRL Atmospheric Variational Data Assimilation System) and NCODA (Navy Coupled Ocean Data Assimilation) for atmosphere and ocean data assimilation. NAVDAS and NCODA are 3D-variational (3DVAR) analysis schemes. The ensembles are generated using separate applications of the Ensemble Transform (ET) technique in both the atmosphere (for moving or non-moving nests) and the ocean. The atmospheric ET is computed using wind, temperature, and moisture variables, while the oceanographic ET is derived from ocean current, temperature, and salinity variables. Estimates of analysis error covariance, which is used as a constraint in the ET, are provided by the ocean and atmosphere 3DVAR assimilation systems. The newly developed system has been successfully tested for a variety of configurations, including differing model resolution, number of members, forecast length, and moving and fixed nest options. Results from relatively coarse resolution (～27-km) ensemble simulations of Hurricanes Hanna and Ike demonstrate that the ensemble can provide valuable uncertainty information about the storm track and intensity, though the ensemble mean provides only a small amount of improved predictive skill compared to the deterministic control member.     

Moisture transport in a soil-plant system: a mathematical model and finite element analysis

A mathematical model and approximate analysis using finite elements are developed to simulate the transport of moisture from a soil medium through a small seedling or plant to the atmosphere. An intrinsic part of the mathematical model is analysis of the characteristic diffusion rates of different components in the soil-plant system. This leads to consideration of the non-linear coupling of the component soil and plant regimes and effective numerical solution of the problem using a Galerkin semi-discrete finite element method: Details concerning the mathematical model and approximate analysis are described. Numerical experiments are conducted to examine the performance of the model. Relaxation and iterative acceleration techniques and an adaptive timestepping strategy are devised to improve the solution algorithms. Comparisons of model predictions with results of laboratory and field experiments indicate that the model provides useful information on plant-water relations.     

山洞环境对地震观测的影响

通过观察形变仪器受到的特殊干扰,进而注意到山洞环境对地震观测的影响.在对干扰事件的起因作深入探讨,推断此类干扰与山洞的温度、湿度相关.进一步分析显示,潮湿是山洞环境发生变化的直观反映,而山洞的温度变化则是山洞环境发生改变的主要原因,是一系列干扰的直接起因.通过对以上影响因素进行研究并试图消除环境干扰,最终可以获得正常的观测结果.由此可见,为获得准确的观测数据,避免此类干扰影响地震分析,需要创造稳定的山洞环境.     

Requirements of a global near-surface soil moisture satellite mission: accuracy,repeat time,and spatial resolution

Soil moisture satellite mission accuracy, repeat time and spatial resolution requirements are addressed through a numerical twin data assimilation study. Simulated soil moisture profile retrievals were made by assimilating near-surface soil moisture observations with various accuracy (0, 1, 2, 3, 4, 5 and 10%v/v standard deviation) repeat time (1, 2, 3, 5, 10, 15, 20 and 30 days), and spatial resolution (0.5, 6, 12 18, 30, 60 and 120 arc-min). This study found that near-surface soil moisture observation error must be less than the model forecast error required for a specific application when used as data assimilation input, else slight model forecast degradation may result. It also found that near-surface soil moisture observations must have an accuracy better than 5%v/v to positively impact soil moisture forecasts, and that daily near-surface soil moisture observations achieved the best soil moisture and evapotranspiration forecasts for the repeat times assessed, with 1–5 day repeat times having the greatest impact. Near-surface soil moisture observations with a spatial resolution finer than the land surface model resolution (∼30 arc-min) produced the best results, with spatial resolutions coarser than the model resolution yielding only a slight degradation. Observations at half the land surface model spatial resolution were found to be appropriate for our application. Moreover, it was found that satisfying the spatial resolution and accuracy requirements was much more important than repeat time.     

Sensitivity of Mesoscale Model Forecast During a Satellite Launch to Different Cumulus Parameterization Schemes in MM5

The identification of the model discrepancy and skill is crucial when a forecast is issued. The characterization of the model errors for different cumulus parameterization schemes (CPSs) provides more confidence on the model outputs and qualifies which CPSs are to be used for better forecasts. Cases of good/bad skill scores can be isolated and clustered into weather systems to identify the atmospheric structures that cause difficulties to the forecasts. The objective of this work is to study the sensitivity of weather forecast, produced using the PSU-NCAR Mesoscale Model version 5 (MM5) during the launch of an Indian satellite on 5th May, 2005, to the way in which convective processes are parameterized in the model. The real-time MM5 simulations were made for providing the weather conditions near the launch station Sriharikota (SHAR). A total of 10 simulations (each of 48 h) for the period 25th April to 04th May, 2005 over the Indian region and surrounding oceans were made using different CPSs. The 24 h and 48 h model predicted wind, temperature and moisture fields for different CPSs, namely the Kuo, Grell, Kain-Fritsch and Betts-Miller, are statistically evaluated by calculating parameters such as mean bias, root-mean-squares error (RMSE), and correlation coefficients by comparison with radiosonde observation. The performance of the different CPSs, in simulating the area of rainfall is evaluated by calculating bias scores (BSs) and equitable threat scores (ETSs). In order to compute BSs and ETSs the model predicted rainfall is compared with Tropical Rainfall Measuring Mission (TRMM) observed rainfall. It was observed that model simulated wind and temperature fields by all the CPSs are in reasonable agreement with that of radiosonde observation. The RMSE of wind speed, temperature and relative humidity do not show significant differences among the four CPSs. Temperature and relative humidity were overestimated by all the CPSs, while wind speed is underestimated, except in the upper levels. The model predicted moisture fields by all CPSs show substantial disagreement when compared with observation. Grell scheme outperforms the other CPSs in simulating wind speed, temperature and relative humidity, particularly in the upper levels, which implies that representing entrainment/detrainment in the cloud column may not necessarily be a beneficial assumption in tropical atmospheres. It is observed that MM5 overestimates the area of light precipitation, while the area of heavy precipitation is underestimated. The least predictive skill shown by Kuo for light and moderate precipitation asserts that this scheme is more suitable for larger grid scale (>30 km). In the predictive skill for the area of light precipitation the Betts-Miller scheme has a clear edge over the other CPSs. The evaluation of the MM5 model for different CPSs conducted during this study is only for a particular synoptic situation. More detailed studies however, are required to assess the forecast skill of the CPSs for different synoptic situations.