Showing papers in "Meteorology and Atmospheric Physics in 2011"

Recent trends of mean maximum and minimum air temperatures in the western half of Iran

Abstract: In this study, the trends of the annual, seasonal and monthly maximum (T max) and minimum (T min) air temperatures time series were investigated for 20 stations in the western half of Iran during 1966–2005. Three statistical tests including Mann–Kendall, Sen’s slope estimator and linear regression were used for the analysis. The annual T max and T min series showed a positive trend in 85% of the stations and a negative trend in 15% of the stations in the study region. The highest increase of T max and T min values were obtained over Kermanshah and Ahwaz at the rates of (+)0.597°C/decade and (+)0.911°C/decade, respectively. On the seasonal scale, the strongest increasing trends were identified in T max and T min data in summer. The highest numbers of stations with positive significant trends occurred in the monthly T max and T min series in August. In contrast, the lowest numbers of stations with significant positive trends were observed between November and March. Overall, the results showed similar increasing trends for the study variables, although T min generally increased at a higher rate than T max in the study period.

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Abstract: Soil temperature (TS) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of TS from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily TS at six depths (5, 10, 20, 30, 50 and 100 cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (Ta), solar radiation (RS), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted TS values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that Ta, RH, RS and P were reasonably correlated with TS at various depths, but the most effective parameters influencing TS at different depths were Ta and RH.

Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model

Abstract: Comprehensive sensitivity analyses on physical parameterization schemes of Weather Research Forecast (WRF-ARW core) model have been carried out for the prediction of track and intensity of tropical cyclones by taking the example of cyclone Nargis, which formed over the Bay of Bengal and hit Myanmar on 02 May 2008, causing widespread damages in terms of human and economic losses. The model performances are also evaluated with different initial conditions of 12 h intervals starting from the cyclogenesis to the near landfall time. The initial and boundary conditions for all the model simulations are drawn from the global operational analysis and forecast products of National Center for Environmental Prediction (NCEP-GFS) available for the public at 1° lon/lat resolution. The results of the sensitivity analyses indicate that a combination of non-local parabolic type exchange coefficient PBL scheme of Yonsei University (YSU), deep and shallow convection scheme with mass flux approach for cumulus parameterization (Kain-Fritsch), and NCEP operational cloud microphysics scheme with diagnostic mixed phase processes (Ferrier), predicts better track and intensity as compared against the Joint Typhoon Warning Center (JTWC) estimates. Further, the final choice of the physical parameterization schemes selected from the above sensitivity experiments is used for model integration with different initial conditions. The results reveal that the cyclone track, intensity and time of landfall are well simulated by the model with an average intensity error of about 8 hPa, maximum wind error of 12 m s−1and track error of 77 km. The simulations also show that the landfall time error and intensity error are decreasing with delayed initial condition, suggesting that the model forecast is more dependable when the cyclone approaches the coast. The distribution and intensity of rainfall are also well simulated by the model and comparable with the TRMM estimates.

An exceptionally heavy snowfall in Northeast china: large-scale circulation anomalies and hindcast of the NCAR WRF model

Abstract: In Northeast China (NEC), snowfalls usually occur during winter and early spring, from mid-October to late March, and strong snowfalls rarely occur in middle spring During 12–13 April 2010, an exceptionally strong snowfall occurred in NEC, with 268 mm of accumulated water-equivalent snow over Harbin, the capital of the most eastern province in NEC In this study, the major features of the snowfall and associated large-scale circulation and the predictability of the snowfall are analyzed using both observations and models The Siberia High intensified and shifted southeastward from 10 days before the snowfall, resulting in intensifying the low-pressure system over NEC and strengthening the East Asian Trough during 12–13 April Therefore, large convergence of water vapor and strong rising motion appeared over eastern NEC, resulting in heavy snowfall Hindcast experiments were carried out using the NCAR Weather Research and Forecasting (WRF) model in a two-way nesting approach, forced by NCEP Global Forecast System data sets Many observed features including the large-scale and regional circulation anomalies and snowfall amount can be reproduced reasonably well, suggesting the feasibility of the WRF model in forecasting extreme weather events over NEC A quantitative analysis also shows that the nested NEC domain simulation is even better than mother domain simulation in simulating the snowfall amount and spatial distribution, and that both simulations are more skillful than the NCEP Global Forecast System output The forecast result from the nested forecast system is very promising for an operational purpose

Observation of snowfall with a low-power FM-CW K-band radar (Micro Rain Radar)

Abstract: Quantifying snowfall intensity especially under arctic conditions is a challenge because wind and snow drift deteriorate estimates obtained from both ground-based gauges and disdrometers. Ground-based remote sensing with active instruments might be a solution because they can measure well above drifting snow and do not suffer from flow distortions by the instrument. Clear disadvan- tages are, however, the dependency of e.g. radar returns on snow habit which might lead to similar large uncertainties. Moreover, high sensitivity radars are still far too costly to operate in a network and under harsh conditions. In this paper we compare returns from a low-cost, low-power vertically pointing FM-CW radar (Micro Rain Radar, MRR) operating at 24.1 GHz with returns from a 35.5 GHz cloud radar (MIRA36) for dry snowfall during a 6-month observation period at an Alpine station (Environmental Research Station Schneefernerhaus, UFS) at 2,650 m height above sea level. The goal was to quantify the potential and limitations of the MRR in relation to what is achievable by a cloud radar. The operational MRR proce- dures to derive standard radar variables like effective reflectivity factor (Ze) or the mean Doppler velocity (W) had to be modified for snowfall since the MRR was originally designed for rain observations. Since the radar returns from snowfall are weaker than from comparable rainfall, the behavior of the MRR close to its detection threshold has been analyzed and a method is proposed to quantify the noise level of the MRR based on clear sky observations. By converting the resulting MRR-Ze into 35.5 GHz equivalent Ze values, a remaining difference below 1 dBz with slightly higher values close to the noise threshold could be obtained. Due to the much higher sen- sitivity of MIRA36, the transition of the MRR from the true signal to noise can be observed, which agrees well with the independent clear sky noise estimate. The mean Doppler velocity differences between both radars are below 0.3 ms -1 . The distribution of Ze values from MIRA36 are finally used to estimate the uncertainty of retrieved snow- fall and snow accumulation with the MRR. At UFS low snowfall rates missed by the MRR are negligible when comparing snow accumulation, which were mainly caused by intensities between 0.1 and 0.8 mm h -1 . The MRR overestimates the total snow accumulation by about 7%. This error is much smaller than the error caused by uncertain Ze-snowfall rate relations, which would affect the MIRA36 estimated to a similar degree.

Interannual and interdecadal variations of the South Asian and western Pacific subtropical highs and their relationships with Asian-Pacific summer climate

Abstract: In this study, interdecadal and interannual variations of the South Asian high (SAH) and the western Pacific subtropical high (WPSH), as well as their relationships with the summer climate over Asian and Pacific regions, are addressed. The variations of SAH and WPSH are objectively measured by the first singular value decomposition (SVD) mode of geopotential heights at the 100- and 500-hPa levels. The first SVD mode of summertime 100- and 500-hPa geopotential heights represents well the relationship between the variations of SAH and WPSH. Both SAH and WPSH exhibit large interannual variability and experienced an apparent long-term change in 1987. The WPSH intensifies and extends westward when SAH intensifies and extends eastward, and vice versa. The India–Burma trough weakens when WPSH intensifies. The changes in SAH and WPSH at various levels are linked to broad-scale increases in tropical tropospheric temperature and geopotential height. When SAH and WPSH strengthen, monsoon flow becomes weaker over eastern Asia. In the meantime, precipitation decreases over eastern South China Sea, Philippines, the Philippine Sea and northeastern Asia, but increases over China, Korea, Japan and the ocean domain east of Japan. Similar features are mostly found on both interdecadal and interannual timescales, but are more evident on interannual timescale.

Spatiotemporal analysis of particulate matter, sulfur dioxide and carbon monoxide concentrations over the city of Rio de Janeiro, Brazil

Abstract: Time series of pollutants and weather variables measured at four sites in the city of Rio de Janeiro, Brazil, between 2002 and 2004, were used to characterize temporal and spatial relationships of air pollution. Concentrations of particulate matter (PM10), sulfur dioxide (SO2) and carbon monoxide (CO) were compared to national and international standards. The annual median concentration of PM10 was higher than the standard set by the World Health Organization (WHO) on all sites and the 24 h means exceeded the standards on several occasions on two sites. SO2 and CO did not exceed the limits, but the daily maximum of CO in one of the stations was 27% higher on weekends compared to weekdays, due to increased activity in a nearby Convention Center. Air temperature and vapor pressure deficit have both presented the highest correlations with pollutant’s concentrations. The concentrations of SO2 and CO were not correlated between sites, suggesting that local sources are more important to those pollutants compared to PM10. The time series of pollutants and air temperature were decomposed in time and frequency by wavelet analysis. The results revealed that the common variability of air temperature and PM10 is dominated by temporal scales of 1–8 days, time scales that are associated with the passage of weather events, such as cold fronts.

Comparison of four ensemble methods combining regional climate simulations over Asia

Abstract: A number of uncertainties exist in climate simulation because the results of climate models are influenced by factors such as their dynamic framework, physical processes, initial and driving fields, and horizontal and vertical resolution. The uncertainties of the model results may be reduced, and the credibility can be improved by employing multi-model ensembles. In this paper, multi-model ensemble results using 10-year simulations of five regional climate models (RCMs) from December 1988 to November 1998 over Asia are presented and compared. The simulation results are derived from phase II of the Regional Climate Model Inter-comparison Project (RMIP) for Asia. Using the methods of the arithmetic mean, the weighted mean, multivariate linear regression, and singular value decomposition, the ensembles for temperature, precipitation, and sea level pressure are carried out. The results show that the multi-RCM ensembles outperform the single RCMs in many aspects. Among the four ensemble methods used, the multivariate linear regression, based on the minimization of the root mean square errors, significantly improved the ensemble results. With regard to the spatial distribution of the mean climate, the ensemble result for temperature was better than that for precipitation. With an increasing number of models used in the ensembles, the ensemble results were more accurate. Therefore, a multi-model ensemble is an efficient approach to improve the results of regional climate simulations.

Assimilation of the multisatellite data into the WRF model for track and intensity simulation of the Indian Ocean tropical cyclones

Abstract: Weather Research and Forecasting (WRF-ARW) model and its three-dimensional variational data assimilation (3D-Var) system are used to investigate the impact of the Quick Scatterometer (QuikSCAT) near surface winds, Special Sensor Microwave/Imager (SSM/I)-derived Total Precipitable Water (TPW), and Meteosat-7-derived Atmospheric Motion Vectors (AMVs) on the track and intensity prediction of tropical cyclones over the North Indian Ocean. The case of tropical cyclone, Gonu (June 2007; Arabian Sea), is first tested and the results show significant improvements particularly due to the assimilation of QuikSCAT winds. Three other cases, cyclone Mala (April 2006; Bay of Bengal), Orissa super cyclone (October 1999; Bay of Bengal), and Very Severe Cyclonic storm (October 1999; Bay of Bengal), are then examined. The prediction of cyclone tracks improved significantly with the assimilation of QuikSCAT winds. The track improvement resulted from the relocation of the initial cyclonic vortices after the assimilation of QuikSCAT wind vectors. After the assimilation of QuikSCAT winds, the mean (for four cyclone cases) track errors for first, second, and third day forecasts are reduced to 72, 101, and 166 km, respectively, from 190, 250, and 381 km of control (without QuikSCAT winds) runs. The assimilation of QuikSCAT winds also shows positive impact on the intensity (in terms of maximum surface level winds) prediction particularly for those cyclones, which are at their initial stages of the developments at the time of data assimilation. The assimilation of SSM/I TPW has significant influence (negative and positive) on the cyclone track. In three of the four cases, the assimilation of the SSM/I TPW resulted in drying of lower troposphere over cyclonic region. This decrease of moisture in TPW assimilation experiment resulted in reduction of cyclonic intensity. In three of the four cyclones, the assimilation of Meteosat-7 AMVs shows negative impact on the track prediction.

Error assessment of climate variables for FAO-56 reference evapotranspiration

Abstract: Meteorological stations, which measure all the required meteorological parameters to estimate reference evapotranspiration (ETo) using the Food and Agriculture Organization Penman–Monteith (FAO56-PM) method, are limited in Korea. In this study, alternative methods were applied to estimate these parameters, and the applicability of these methods for ETo estimation was evaluated by comparison with a complete meteorological dataset collected in 2008 in Korea. Despite differences between the estimation and observation of radiation and wind speed, the comparison of ETo showed small differences [i.e., mean bias error (MBE) varying −0.22 to 0.25 mm day−1 and root-mean-square-error (RMSE) varying 0.06–0.50 mm day−1]. The estimated vapor pressure differed considerably from the observed, resulting in a larger discrepancy in ETo (i.e., MBE of −0.50 mm day−1 and RMSE of 0.60–0.73 mm day−1). Estimated ETo showed different sensitivity to variations of the meteorological parameters—in order of vapor pressure > wind speed > radiation. It is clear that the FAO56-PM method is applicable for reasonable ETo estimation at a daily time scale especially in data-limited regions in Korea.

Daily total global solar radiation modeling from several meteorological data

Abstract: This paper investigates the modeling of the daily total global solar radiation in Adana city of Turkey using multi-linear regression (MLR), multi-nonlinear regression (MNLR) and feed-forward artificial neural network (ANN) methods Several daily meteorological data, ie, measured sunshine duration, air temperature and wind speed and date of the year, ie, monthly and daily, were used as independent variables to the MLR, MNLR and ANN models In order to determine the relationship between the total global solar radiation and other meteorological data, and also to obtain the best independent variables, the MLR and MNLR analyses were performed with the “Stepwise” method in the Statistical Packages for the Social Sciences (SPSS) program Thus, various models consisting of the combination of the independent variables were constructed and the best input structure was investigated The performances of all models in the training and testing data sets were compared with the measured daily global solar radiation values The obtained results indicated that the ANN method was better than the other methods in modeling daily total global solar radiation For the ANN model, mean absolute error (MAE), mean absolute percentage error (MAPE), correlation coefficient (R) and coefficient of determination (R 2) for the training/testing data set were found to be 089/100 MJ/m2 day, 788/923%, 09824/09751, and 09651/09508, respectively

Simulations of Cyclone Sidr in the Bay of Bengal with a high-resolution model: sensitivity to large-scale boundary forcing

Abstract: The predictability of Cyclone Sidr in the Bay of Bengal was explored in terms of track and intensity using the Advanced Research Hurricane Weather Research Forecast (AHW) model. This constitutes the first applica- tion of the AHW over an area that lies outside the region of the North Atlantic for which this model was developed and tested. Several experiments were conducted to understand the possible contributing factors that affected Sidr's intensity and track simulation by varying the initial start time and domain size. Results show that Sidr's track was strongly controlled by the synoptic flow at the 500-hPa level, seen especially due to the strong mid-latitude wes- terly over north-central India. A 96-h forecast produced westerly winds over north-central India at the 500-hPa level that were notably weaker; this likely caused the modeled cyclone track to drift from the observed actual track. Reducing the model domain size reduced model error in the synoptic-scale winds at 500 hPa and produced an improved cyclone track. Specifically, the cyclone track appeared to be sensitive to the upstream synoptic flow, and was, therefore, sensitive to the location of the western boundary of the domain. However, cyclone intensity remained largely unaffected by this synoptic wind error at the 500-hPa level. Comparison of the high resolution, moving nested domain with a single coarser resolution domain showed little difference in tracks, but resulted in significantly different intensities. Experiments on the domain size with regard to the total precipitation simulated by the model showed that precipitation patterns and 10-m surface winds were also different. This was mainly due to the mid-latitude westerly flow across the west side of the model domain. The analysis also suggested that the total precipitation pattern and track was unchanged when the domain was extended toward the east, north, and south. Furthermore, this highlights our conclusion that Sidr was influenced from the west side of the domain. The dis- placement error was significantly reduced after the domain size from the western model boundary was decreased. Study results demonstrate the capability and need of a high-resolution mesoscale modeling framework for simu- lating the complex interactions that contribute to the for- mation of tropical cyclones over the Bay of Bengal region.

Relations between atmospheric circulation and precipitation in Belgium

Abstract: The objective Lamb circulation type (CT) classification method, based on the strength, direction and vorticity of the geostrophic flow, is applied to Belgium. Eleven different large-scale synoptic circulation patterns are derived on a daily scale for the period 1962 and 1999. The circulation patterns are subsequently related to precipitation amount and occurrence for six stations characterising different regions in Belgium, namely coastal, flat and hilly areas. Based on precipitation occurrence and intensity, five wet classes are defined, which are responsible for 83% of the total precipitation amount. It is shown that a regression model based on CT as predictors represents precipitation variability better in winter and autumn than in spring and summer. On the monthly scale and in winter, CTs explain 60.3% of the precipitation variability.

The assessment of aerosol optical properties over Mohal in the northwestern Indian Himalayas using satellite and ground-based measurements and an influence of aerosol transport on aerosol radiative forcing

Abstract: The present study deals with the aerosol optical properties which are assessed during the period 2007 to 2009 over Mohal (31.9oN, 77.12oE) in the northwestern Indian Himalaya, using ground-based measurements and multi-satellite data. The daily average value of aerosol optical depth (AOD) at 500 nm, Angstrom exponent and turbidity coefficient are 0.24 ± 0.08, 1.02 ± 0.34 and 0.13 ± 0.05, respectively. The comparative study of satellite and ground-based measurements reveals that the percentage retrieval for daily AOD at 550 nm over Mohal within the expected accuracy (Δτ pλ = ±0.05 ± 0.15τ pλ ) is around 87%, with a significant correlation coefficient of 0.76. The present study suggests that the retrieval of AOD through satellite data is able to characterise the distribution of AOD over Mohal. However, further efforts are needed in order to eliminate systematic errors in the existing Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm. The transport of desert dust and anthropogenic aerosol during high aerosol loading days caused a significant reduction in surface-reaching solar radiation by 149 and 117%, respectively. This large reduction in surface-reaching solar radiation increased the atmospheric heating rate by 0.93 and 0.72 K day−1, respectively. This study indicates significant climatic implications due to the transport of aerosols in the northwestern Indian Himalaya.

Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms

Abstract: The aim of the present study is to develop an adaptive neuro-fuzzy inference system (ANFIS) to forecast the peak gust speed associated with thunderstorms during the pre-monsoon season (April–May) over Kolkata (22°32′N, 88°20′E), India. The pre-monsoon thunderstorms during 1997–2008 are considered in this study to train the model. The input parameters are selected from various stability indices using statistical skill score analysis. The most useful and relevant stability indices are taken to form the input matrix of the model. The forecast through the hybrid ANFIS model is compared with non-hybrid radial basis function network (RBFN), multi layer perceptron (MLP) and multiple linear regression (MLR) models. The forecast error analyses of the models in the test cases reveal that ANFIS provides the best forecast of the peak gust speed with 3.52% error, whereas the errors with RBFN, MLP, and MLR models are 10.48, 11.57, and 12.51%, respectively. During the validation with the 2009 observations of the India Meteorological Department (IMD), the ANFIS model confirms its superiority over other comparative models. The forecast error during the validation of the ANFIS model is observed to be 3.69%, with a lead time of <12 h, whereas the errors with RBFN, MLP, and MLR are 12.25, 13.19, and 14.86%, respectively. The ANFIS model may, therefore, be used as an operational model for forecasting the peak gust speed associated with thunderstorms over Kolkata during the pre-monsoon season.

The analysis and impact of simulated high-resolution surface observations in addition to radar data for convective storms with an ensemble Kalman filter

Abstract: Observing system simulation experiments are performed using an ensemble Kalman filter to investigate the impact of surface observations in addition to radar data on convective storm analysis and forecasting. A multi-scale procedure is used in which different covariance localization radii are used for radar and surface observations. When the radar is far enough away from the main storm so that the low level data coverage is poor, a clear positive impact of surface observations is achieved when the network spacing is 20 km or smaller. The impact of surface data increases quasi-linearly with decreasing surface network spacing until the spacing is close to the grid interval of the truth simulation. The impact of surface data is sustained or even amplified during subsequent forecasts when their impact on the analysis is significant. When microphysics-related model error is introduced, the impact of surface data is reduced but still evidently positive, and the impact also increases with network density. Through dynamic flow-dependent background error covariance, the surface observations not only correct near-surface errors, but also errors at the mid- and upper levels. State variables different from observed are also positively impacted by the observations in the analysis.

Processing of Indian Doppler Weather Radar data for mesoscale applications

Abstract: This paper demonstrates the usefulness of Indian Doppler Weather Radar (DWR) data for nowcasting applications, and assimilation into a mesoscale Numerical Weather Prediction (NWP) model. Warning Decision Support System Integrated Information (WDSS-II) developed by National Severe Storm Laboratory (NSSL) and Advanced Regional Prediction System (ARPS) developed at the Centre for Analysis and Prediction, University of Oklahoma are used for this purpose. The study reveals that the WDSS-II software is capable of detecting and removing anomalous propagation echoes from the Indian DWR data. The software can be used to track storm cells and mesocyclones through successive scans. Radar reflectivity mosaics are created for a land-falling tropical cyclone—Khaimuk of 14 November 2008 over the Bay of Bengal using observations from three DWR stations, namely, Visakhapatnam, Machilipatnam and Chennai. Assimilation of the quality-controlled radar data (DWR, Chennai) of the WDSS-II software in a very high-resolution NWP model (ARPS) has a positive impact for improving mesoscale prediction. This has been demonstrated for a land-falling tropical cyclone Nisha of 27 November 2008 of Tamil Nadu coast. This paper also discusses the optimum scan strategy and networking considerations. This work illustrates an important step of transforming research to operation.

Objective identification of synoptic meteorological patterns favouring African dust intrusions into the marine boundary layer of the subtropical eastern north Atlantic region

Abstract: Synoptic geopotential height anomalies patterns favouring African dust outbreaks into the marine boundary layer (MBL) of the subtropical Eastern North Atlantic Region (SENAR) were objectively identified. The proportion of the total variance explained by each of these patterns was also calculated. Dust intrusions into the MBL of the SENAR were identified using total suspended particles (TSP) data at a rural background station in Tenerife Island (El Rio station, ER). Geopotential height anomalies at 1,000, 850, 700 and 500 hPa, respectively, in days of African dust intrusion in the period 1998–2003 were grouped in monthly sets. Two different but complementary methods (K-means and Principal Components) were applied to daily geopotential height anomalies for each month and for each pressure level in case of African dust intrusion. Three principal geopotential height anomalies patterns were found. Type I consist on a high-pressure system over Europe that affects North Africa, occasionally giving rise to a ridge. The Canary Islands are in the south-west flank of this high-pressure system. This pattern is dominant throughout the whole year. Type II and type III patterns consist on a low located to the northeast and southeast of the Canary Islands, respectively, coupled with a high over the Mediterranean basin and/or North Africa. Two case analyses are presented, as well as a systematic validation of the meteorological pattern classification for all dust intrusions detected at ER station within the period 2004–2007.

Expansion of the planar-fit method to estimate flux over complex terrain

Abstract: An expanded planar-fit (PF) method over complex terrain is presented and applied to coordinate rotation of the eddy-covariance (EC) flux and vertical velocity estimation. Theoretical analysis indicates that PF coefficients depend on wind direction, and an expression of vertical velocity is deduced. We applied the theory using 1 year of observations from the KoFlux site in the Gwangneung Forest in Korea and investigated the influence of wind direction on the PF method. Then, we performed an expansion of the PF method to consider dependence of PF coefficients on wind direction and applied the PF method to every sector. The results show that the PF coefficients and tilt angles over complex terrain vary with wind direction. Two hundred 30-min data sets are sufficient to derive stable PF coefficients over hilly terrain for each sector. The relative difference in eddy-covariance flux between the general planar fit (GPF) and sector planar fit (SPF) is less than 10% for the scalar flux and about 18% for friction velocity. Vertical velocity and vertical advection (VA) terms were also calculated and compared using SPF and GPF methods, and a normal distribution and diurnal trend of real vertical velocity on clear days are presented.

The effect of the Zagros Mountains on the formation and maintenance of the Iran Anticyclone using RegCM4

Abstract: Iran anticyclone is one of the main features of the summer circulation over the Middle East in the middle and upper troposphere. To examine the effect of the Zagros Mountains on the formation and maintenance of the Iran anticyclone, an experiment was conducted by Regional Climate Model (RegCM4) in an area between 22°–44°N and 35°–70°E with a 40 km horizontal grid spacing. The NCEP/NCAR re-analysis data set were used to provide the initial and lateral boundary conditions in a control run and in a simulation run by removing the Zagros Mountains. The result reveals that the Zagros Mountains have an important effect on the formation and maintenance of the low-level cyclonic circulation and mid-level anticyclonic circulation in summer. Examining the diabatic heating shows that the elimination of the Zagros Mountains causes a significant change in the heating values and its spatial distributions over the study area. Comparing the diabatic heating terms, the vertical advection term has the main contribution to the total heating. In the absence of the Zagros Mountains, the vertical advection and the mid-troposphere anticyclonic circulation are apparently weak and, therefore, the Iran subtropical anticyclone vanishes over the west of Iran. The study indicates that the Zagros Mountains as an elevated heat source have the main impact in the formation of a thermally driven circulation over the Middle East.

Initiation of deep convection caused by land-surface inhomogeneities in West Africa: a modelled case study

Abstract: Simulations with the Consortium for Small Scale Modelling model were performed to investigate the impact of land surface inhomogeneities on the initiation of convection. A case from the African Monsoon Multidisciplinary Analysis campaign, 11 June 2006, was selected. On this day, a mesoscale convective system was observed and simulated. The simulation scenarios included a realistic and an increased initial soil moisture distribution as well as a homogeneous soil moisture and texture field. Land use and orography were the same in all runs. Heat and moisture budget calculations were applied to analyse the processes responsible for the evolution of pre-convective atmospheric conditions and convection-triggering thermally induced circulation systems. Convective cells were initiated in all experiments. However, the amount of cells, their origin, evolution, and precipitation amount differed. First shallow clouds were initiated over areas with higher sensible heat fluxes. The evolution of subsequent deep convection was triggered by secondary circulation systems caused by baroclinic conditions generated by clouded and unclouded regions. The further evolution of the precipitation cells strongly depended on convective inhibition in the areas the cells moved into.

Assessment of GEWEX/SRB version 3.0 monthly global radiation dataset over China

Abstract: NASA/GEWEX (National Aeronautics and Space Administration/Global Energy and Water Cycle Experiment) Surface Radiation Budget (SRB) has released its latest radiation dataset, version 3.0. We examine the accuracy of the monthly mean global radiation in China using surface-observed radiation (SOR) data at 42 stations during the period 1984–2004. Overall comparison shows a general overestimation of satellite retrieval radiation data with a bias of 14.6 W m−2 and a root mean square error of 25.9 W m−2. Differences at individual stations suggested satellite data are consistently higher than surface measurements over eastern China (110°E), but occasional underestimation occurs in Western China, especially Southwest China. Intra-annual variation analysis indicates that SRB satellite radiation can capture the annual cycle well. For trend of global radiations, there are evident discrepancies between satellite retrievals and surface measurements for both the entire period and segmental terms. For the entire period from 1984 to 2004, most stations show a positive trend based on surface measurements, while the majority of collocated pixels show a negative trend. Segmental trends demonstrated that the principal difference occurred during the first period of 1981–1994. After 1994, the two datasets change similarly. Therefore, trend analysis in terms of detecting global dimming/brightening remains very difficult as surface measurements and satellite products do not agree yet. In addition, some proposals are made towards better understanding of the bias of satellite products and to improve further the satellite retrieval algorithm with better representation of both cloud and aerosol properties.

Sharp depletion of atmospheric refractive index associated with Nor’wester over Gangetic West Bengal: a possible method of forecasting Nor’wester

Abstract: The paper presents the nature of the variation of refractive index of atmospheric medium with time and altitude before, during and after the onset of thunderstorms over Gangetic West Bengal during the pre-monsoon period. A critical analysis shows that sharp depletion of the refractive index takes place before the onset of Nor’westers and possible explanations are also offered for the said occurrence.

Autocorrelation properties of the sunshine number and sunshine stability number

Abstract: Two new Boolean parameters are defined: the sunshine number (related to the state of the sky) and the sunshine stability number (which is as a measure of the fluctuation of the radiative regime). Elementary statistical and sequential properties of both parameters are presented in this paper. Actinometric and meteorological data measured at 15 s lag during 2009 in Timisoara (Romania, southeastern Europe) are used. The yearly series of daily averaged sunshine number has negative skewness and kurtosis. The series of daily averaged sunshine stability number has positive skewness and kurtosis. The series of daily averaged values of sunshine number are best described by an ARIMA(0,1,2) model. ARIMA(0,1,0) and ARIMA(0,2,0) models (associated with an appropriately defined white noise) may be used for synthesis of the sunshine number time series. The first model is to be preferred for practical reasons. The series of daily averaged values of sunshine stability number are best described by an ARIMA(2,2,1) model. The ARIMA(0,0,0) model is recommended to be used for generating time series of sunshine stability number. This model may be used for any particular day during the year and the only parameter depending on the day is the white noise standard deviation. A relationship between the white noise standard deviation and the daily averaged sunshine stability number is proposed.

Signal simplification and cloud detection with an improved Douglas-Peucker algorithm for single-channel lidar

Abstract: Single-channel lidar is a widely used system in atmospheric aerosol and cloud detection. However, many difficulties remain in the automatic and accurate identification of cloud from backscatter signals. Popular methods have been proposed, but there is still large uncertainty in cloud detection, especially when the signal-to-noise ratio is low. In this study, a signal simplification approach based on an improved Douglas-Peucker algorithm is proposed. The layer base and top are then selected using the simplified signal and the raw range-corrected signal. Finally, we use a peak-to-base ratio function to distinguish a cloud layer from a non-cloud layer. The detection results of our algorithm are remarkably better than those obtained using the differential zero-crossing method.

Estimation of the monthly and annual mean maximum and mean minimum air temperature values in Greece

Abstract: In this study, regression equations to estimate the monthly and annual values of the mean maximum and mean minimum air temperatures in Greece are derived. For this purpose, data from 87 meteorological stations distributed all over Greece are used. Geographical parameters, i.e., altitude, latitude, longitude, minimum distance from the sea and an index of terrain morphology, are used as independent variables. These equations explain 79–97% of the variance of the temperature values and have standard error of estimate between 0.59 and 1.20°C. Data from 37 other meteorological stations are used to validate the accuracy of the equations. Topographic or climatic factors, which could not be introduced into the equations, are responsible for most temperature residuals >0.5°C or <−0.5°C. Moreover, some particular emphasis has been given to the values of the regression coefficient for the altitude, since it is the estimator for the mean lapse rate of air temperature.

Numerical experiments investigating the orographic effects on a heavy rainfall event over the northwestern coast of Taiwan during TAMEX IOP 13

Abstract: An unusual heavy coastal rainfall event (>231 mm day−1) occurred during the period of 24–25 June 1987 over the lowland (elevation less than 200 m) and coastal areas in northwest and central Taiwan. The Weather Research and Forecasting (WRF) model is used to investigate the role of synoptic forcing, orographic effects and the diurnal heating cycle on the generation of a prefrontal localized low-level convergence zone offshore leading to the observed coastal rainfall maximum. This case is well simulated by the control experiment initialized at 0000 UTC (0800 LST) 24 June 1987 using the European Centre for Medium-Range Weather Forecasts data. A model sensitivity test without Taiwan’s terrain fails to reproduce the observed coastal rainfall maximum. It is apparent that for this case, synoptic forcing by the Mei-Yu jet/front system is inadequate to initiate deep convection leading to the development of coastal heavy precipitation. The generation of the localized low-level convergence zone is closely related to the simulated strong winds with a large southwesterly wind component (or the barrier jet) along the northwestern coast as the surface front approaches. The development of the simulated barrier jet is due to a 50–60% increase in the meridional pressure gradient as a result of orographic blocking. The diurnal heating cycle also impacts the strength of the simulated barrier jet over the northwestern Taiwan coast. The simulated barrier jet is stronger (~3 m s−1) in the early morning than in the afternoon as orographic blocking is most significant when the surface air is the coldest. The representation of the terrain in the model impacts the simulated barrier jet and rainfall. With a coarse horizontal resolution (45 km), orographic blocking is less significant than the control run with a much weaker meridional wind component over the northwestern coast of Taiwan. The coarse resolution model fails to reproduce the observed rainband off the northwestern coast. Thus, to successfully simulate this type of event, high-resolution mesoscale models adequately depicting Taiwan’s terrain are required.

Impact of background error statistics on 3D-Var assimilation: case study over the Indian region

Abstract: The quality of background error statistics (BES) is one of the key components for successful assimilation of observations in a numerical model. Considerable uncertainties and non-uniqueness exist, however, in prescribing BES; in particular, the prescription and impact of BES can also depend on the weather regime and not much is known in this regard over the Indian region. We have conducted a series of assimilation experiments using the WRF three-dimensional variational data assimilation (3D-Var) system with different BES to asses the relative improvement in model forecast due to different BES over the Indian region. The forecasted wind, temperature, and humidity are verified against NCEP analysis and conventional radiosondes, while the predicted rainfall is verified against Tropical Rainfall Measuring Mission (TRMM) observations. Using a number of parameters to quantify impact of BES, it is shown that the use of regional BES (RBES) in WRF 3D-Var significantly improves model forecast as compared to the control experiment (no assimilation) and Global BES (GBES). The use of RBES from National Meteorological Center (NMC) and ensemble perturbation (ENS) method in WRF 3D-Var produced similar impact on model forecasts except slight differences in wind speed. This study highlights the importance of domain-dependent and region-specific BES in WRF 3D-Var assimilation system; for the selected events, results obtained using RBES is found to be significantly better than GBES.

Spatial gridding of daily maximum and minimum temperatures in Europe

Abstract: We developed an operationally applicable land-only daily high-resolution (5 km × 5 km) gridding method for station observations of minimum and maximum 2 m temperature (T min/T max) for Europe (WMO region VI). The method involves two major steps: (1) the generation of climatological T min/T max maps for each month of the year using block regression kriging, which considers the spatial variation explained by applied predictors; and (2) interpolation of transformed daily anomalies using block kriging, and combination of the resulting anomaly maps with climatological maps. To account for heterogeneous climatic conditions in the estimation of the statistical parameters, these steps were applied independently in overlapping climatic subregions, followed by an additional spatial merging step. Uncertainties in the gridded maps and the derived error maps were quantified: (a) by cross-validation; and (b) comparison with the T min/T max maps estimated in two regions having very dense temperature observation networks. The main advantages of the method are the high quality of the daily maps of T min/T max, the calculation of daily error maps and computational efficiency.

A numerical simulation of the effect of the number concentration of cloud droplets on Typhoon Chanchu

Abstract: In recent years, an increase in the number of anthropogenic aerosol particles has raised the global mean content of aerosol particles in the atmosphere from that of preindustrial times. The indirect effects of aerosols on weather and climate cannot be ignored. In this paper, the fifth generation Pennsylvania State University (PSU)–National Center of Atmospheric Research (NCAR) Nonhydrostatic Mesoscale Model (MM5) is used to simulate Typhoon Chanchu (international designation: 0601), which affected the northwest Pacific. Simulations are conducted in three two-way nested domains with Mercator map projection. The horizontal grid resolutions of the three domains are 27, 9, and 3 km. A period of 60 h is simulated. Surface and rawinsonde conventional observation data and ocean wind data are additionally incorporated into the initialization data. A control (CTL) experiment is run to produce a reasonable forecast. We change the parameter of the cloud condensation nuclei (CCN) concentration (CNP) in the Reisner-2 scheme of the CTL experiment (the default value is 100 cm−3) to conduct two sensitivity experiments. They are the very clean marine (VCM) CNP experiment (CNP = 25 cm−3) and the severe contamination (SC) CNP experiment (CNP = 1,000 cm−3). We investigate the effects of the CNP on Typhoon Chanchu by comparing and analyzing the simulation results of the three experiments in terms of the track, intensity, precipitation, vertical structure, and microphysical processes. The main results show that Typhoon Chanchu slightly weakens as the CNP increases. Increasing the CCN to 1,000 cm−3 results in less graupel, rainwater, and cloud ice but more cloud water. However, the mixing ratio of snow does not distinctly change as the CNP changes. Increasing the CCN leads a rapid decrease in the autoconversion of cloud water to rainwater. There is no autoconversion of cloud water to rainwater in a seriously polluted continental air mass. As the CNP increases, there is more condensation, evaporation, accretion of cloud water by rainwater, and precipitation fallout. Finally, a seriously polluted continental air mass can result in distinctly lower precipitation efficiency.