VARIABILITY CHARACTERISTICS OF MONTHLY PRECIPITATION IN CENTRAL OKLAHOMA1

ABSTRACT: To fully take advantage of regional climate forecast information for agricultural applications, the relationship between divisional and station scale precipitation characteristics must be quantified. The spatial variability of monthly precipitation is assumed to consist of two components: a systematic and a random component. The systematic component is defined by differences in long-term mean precipitation between stations within a climate division, and the random component by differences between station and divisional standardized values. For the Central Climate Division of Oklahoma, the systematic component has a positive precipitation gradient from west to east with a slope ranging between 3 to 16 mm of precipitation per 100 km depending on the month of the year. On the other hand, the random component ranges between 27 to 48 percent of the mean temporal variation of the monthly precipitation. This significant random spatial variability leads to large localized departures from divisional values, and clearly demonstrates the critical influence of the random component in the utilization of divisional climate forecasts for local agricultural applications. The results of this study also provide an uncertainty range for local monthly precipitation projections that are derived from divisional climate information.     

ESTIMATES OF RUNOFF USING WATER-BALANCE AND ATMOSPHERIC GENERAL CIRCULATION MODELS1

ABSTRACT: The effects of potential climate change on mean annual runoff in the conterminous United States (U.S.) are examined using a simple water-balance model and output from two atmospheric general circulation models (GCMs). The two GCMs are from the Canadian Centre for Climate Prediction and Analysis (CCC) and the Hadley Centre for Climate Prediction and Research (HAD). In general, the CCC GCM climate results in decreases in runoff for the conterminous U.S., and the HAD GCM climate produces increases in runoff. These estimated changes in runoff primarily are the result of estimated changes in precipitation. The changes in mean annual runoff, however, mostly are smaller than the decade-to-decade variability in GCM-based mean annual runoff and errors in GCM-based runoff. The differences in simulated runoff between the two GCMs, together with decade-to-decade variability and errors in GCM-based runoff, cause the estimates of changes in runoff to be uncertain and unreliable.     

PRECIPITATION DIFFERENCES AMONGST GCMs USED FOR THE U.S. NATIONAL ASSESSMENT1

ABSTRACT: Two general circulation models (GCMs) used in the U.S. national assessment of the potential consequences of climate variability and change (CGCM1 and HadCM2) show a large increase in precipitation in the future over the southwestern U.S., particularly during winter. This precipitation increase is an extension of a larger region of increased precipitation in the Pacific Ocean off the west coast of North America that is associated with a deepened and southward-shifted Aleutian Low, a weaker subtropical high, and warmer sea surface temperatures (SSTs). The models differ in their simulation of precipitation anomalies over the southeastern U.S., with CGCM1 showing drier conditions and HadCM2 showing wetter conditions in the future. While both models show decreased frequency of Atlantic storms, consistent with decreased meridional and land/sea temperature gradients, the more coastal position of the storm track in CGCM1 results in less precipitation than modern along the eastern seaboard of the U.S. During summer, differences in land surface models within the two GCMs sometimes lead to differences in soil moisture that feed back to the precipitation over land due to available moisture.     

南京地区近地面臭氧浓度与气象条件关系研究

通过分析2013—2015年南京地区相关气象要素对近地面臭氧浓度的影响,建立了用于不同季节高浓度臭氧污染事件的预报预警模型,并归纳总结了南京地区高浓度臭氧出现的天气形势.结果表明,近地面臭氧浓度的变化与气象要素密切相关,气温、能见度、日照小时、总(净)辐射辐照度等要素与O_3浓度呈显著正相关,与相对湿度、总(低)云量呈负相关.高浓度臭氧污染是多因子综合作用的结果,典型气象条件表现为:太阳辐射强,低云量少,相对湿度适宜,地面小风速及特定的风向.通过定义高浓度臭氧潜势指数HOPI和风向指数WDI,并综合考虑14:00地面气温、相对湿度及8:00各标准层的相关气象要素,建立了逐季节多指标叠套的高浓度臭氧预报方程.采用2016年资料对其进行检验,发现预报值与观测值的相关系数分别达0.72(冬季)、0.76(春季)和0.73(夏季),说明方程具有较好的拟合效果和可预报性.通过普查历史天气图,归纳了伴随南京地区高浓度臭氧事件出现的8种主要天气形势,即高压类(高压中心G0、高压后部G1)、低压类(低压底部D0、低压前部D1、低压倒槽D2)、均压类(高压相关的均压JG、低压相关的均压JD、其它均压J).其中,以高压后部地面形势出现概率最大,低压前部均压场出现时对应臭氧平均浓度最高.     

2013—2015年北京污染频发期细颗粒物重污染成因与天气形势关系的研究

我国重污染呈现愈演愈烈态势,重污染事件在供暖季节（污染频发期）尤为频发.本文利用北京2013—2015年采暖期逐小时PM_2.5浓度数据、再分析资料、气团后向轨迹、气溶胶雷达数据以及探空数据综合分析了北京地区重污染状况,概括了重污染发生时常见的天气形势,探讨了重污染形成原因与天气形势的关系.研究结果表明：2013—2015年采暖期北京发生重污染（日均PM_2.5浓度大于150 μg·m^-3）的天数分别为36、28及35 d,即北京采暖期21.9%的天数受重污染天气影响.2月份重污染事件最为频发,发生频次为27.3%.北京发生重污染事件时,地面被高压控制时,高空500 hPa多东移的槽脊,当位于脊后槽前时,为上升运动,西南风,850 hPa多暖平流,西南风输送暖湿气流,湿度较大,地面偏南风,可能会存在污染物的输送;地面为低压控制时,500 hPa一般为稳定的西风气流或西北气流,低空850 hPa可能存在暖平流,地面常伴随弱的风场辐合,导致污染物累积;当地面为均压场时,高空500 hPa多为脊后槽前的形势,低空无明显冷暖平流,地面等压线稀疏或无等压线,静风天气.这3类结构引发的重污染天数分别占总重污染天数的47.3%、18.2%及34.5%.进一步分析重污染成因与天气形势关系表明：北京地面受高压系统控制时,污染时间持续最长,也最为频发（47.3%）,PM_2.5平均浓度最高可达258.8 μg·m^-3,且常伴随来自西南方向的污染物输送,北京上空1 km附近存在逆温和逆湿.对污染传输路径研究发现：主要存在3条输送通道,①天津-廊坊-北京、②沧州-廊坊-北京、③石家庄-保定-北京.鉴于目前数值模式对天气形势的预报较为成熟,本文对区域重污染过程与天气形势之间的关系研究,有助于为北京地区空气质量的精准预报预警提供科学支持.     

A SIMULATION OF RIVER-BASIN RESPONSE TO MESOSCALE METEOROLOGICAL FORCING: THE SUSQUEHANNA RIVER BASIN EXPERIMENT (SRBEX)1

ABSTRACT: A mesoscale meteorological model, a surface hydrology model, and a ground-water hydrology model are linked to simulate the hydrographic response of a large river basin to a single storm. Synoptic climatology is employed to choose a representative hydro-climatic event. The mesoscale meteorological model uses three nested domains to simulate relatively high-resolution precipitation over a sub-basin of the Susquehanna River Basin. The hydrology models simulate surface runoff and ground-water baseflow using both analyzed and simulated precipitation. The hydrologic abstractions are handled using both Curve Number and Green-Ampt routines. To support the linkage of the numerical models, special attention is given to data resampling and reprojection. The mesoscale meteorological model simulation captures the spatial and temporal structure of the storm event, while the hydrology models represent the timing of the event well. The Curve Number method generates a realistic hydrograph with both analyzed and simulated precipitation. In contrast, the hydrographic response generated by the Green-Ampt routine is inferior. Several interrelated factors contribute to these results, including: the nature of the precipitation event chosen for the experiment; the tendency of the mesoscale meteorological model to underpredict low intensity, widespread precipitation in this case; and the influence of the surface soil-texture characteristics on infiltration rates.     

LAND SURFACE PARAMETERIZATIONS OF GCMs AND MACROSCALE HYDROLOGICAL MODELS1

ABSTRACT: Climatic variation and the possibility of anthropogenically-caused climatic change have emphasized the need for global hydrological cycle models able to simulate the impacts of climate on the atmosphere, continents and oceans. To date, global atmospheric and oceanic models have been developed but, to the best of the author's knowledge, there are no continental hydrological models. Instead, hydrological models continue to develop at the catchment scale and the land phase component of the global hydrologic cycle is modeled as parameterizations within atmospheric models. The author argues that this is not the best solution; that the present land surface components of atmospheric models do not accurately model land phase hydrology and that, instead, atmospheric and oceanic models should be linked to continental-scale hydrological models to form a true model of the global hydrological cycle.     

PREDICTION OF MEAN ANNUAL FLOWS IN NORTH AND CENTRAL FLORIDA1

ABSTRACT: A frequency analysis approach for the prediction of flow characteristics at ungaged locations is applied to a region of high annual precipitation and low topography in north and central Florida. Stationary time series of annual flows are fitted with the lognormal distribution and estimated parameters of the distribution are fitted by third order trend surfaces. These explain 65 and 74 percent of the observed variances in the mean and standard deviation, respectively. Predictions of parameters are then made for several locations previously unused in the study and they are used to estimate the return periods of various flows from the lognormal distribution. Application of the Kolmogorov-Smirnov goodness-of-fit test suggests that only one of the five test stations can be considered significantly different from the observed data, confirming the applicability of this technique.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

The creation of an historical meteorological database for environmental dose assessment

The focus of this study is to develop wind data for the SavannahRiver Site (SRS) between 1955 and 1961 to be used in an assessment of estimates of atmospheric dispersion and downwindrisk at the Savannah River Site. In particular, a study of theuncertainties of radioiodine dosimetry from the late 1950sprovides the underlying motivation for developing historicalwindroses at the Savannah River Site (SRS). Wind measurement towers did not exist at the SRS until theearly 1970s. Three relatively simple methods were used to createa 1955–1961 meteorological database for the SRS for a dosereconstruction project. The winds were estimated from onsitemeasurements in the 1990s and National Weather Service (NWS)observations in the 1990s and 1950s using (1) a linear regressionmethod, (2) a similarity theory approach, and (3) a simplestatistical differences method. The criteria for determining success were based on (1) howwell the mean values and standard deviations of the predictedwind speed agree with the known SRS values from the 1990s, (2) the shape of the predicted frequency distribution functions forwind speed, and (3) how closely the predicted windroses resembledthe SRS windrose for the 1990s. The linear regression model's wind speed distribution functionwas broad, flat, and skewed too much toward higher wind speeds.The similarity theory approach produced a wind speed distributionfunction that contained excess predicted speeds in the range 0–1.54 m s^-1 (0–3 kts) and had `excluded' bins caused bypredictions being made from integer values of knots in the NWSdata. The distribution function from the mean difference methodwas smooth with a shape like a Weibull distribution with a shapeparameter of 2 and appearedto resemble closely the SRS 1992–1996 distribution.The wind directions for all three methods of approach weresuccessfully based on the mean difference method. It wasdifficult to discern differences among the wind roses produced bythe three methods so the wind speed distribution functions needto be examined in order to make an informed choice for dose reconstruction.