基于一维变分算法的地基微波辐射计遥感大气温湿廓线研究

为研究地基微波辐射计遥感温、湿度廓线的一维变分算法的反演能力,用北京地区2010—2011年00和12时(世界时)的多通道地基微波辐射计亮温资料进行试验。首先,利用同时次的地面观测资料、红外亮温(由地基微波辐射计自带红外传感器测得)及探空观测数据,给出提取无云样本的方案,得到432个无云样本;再以辐射传输模式计算得到的模拟亮温为参考,对无云条件下的观测亮温进行质量控制;然后利用探空数据进行模拟试验,结果发现,一维变分算法对3 km以下的温度廓线有较大调整。使反演结果更加接近探空,而对湿度廓线在0—10 km都有不同程度的优化;最后利用一维变分算法对地基微波辐射计观测亮温进行大气温湿廓线反演,将结果与探空对比可以看出,温度廓线的均方根误差小于2.9 K,绝对湿度的均方根误差小于0.47 g/m~3;进一步与地基微波辐射计自带神经网络的反演结果比较表明,一维变分的反演结果更接近实际大气。     

石河子绿洲空气湿度和降水的长期变化趋势分析

分析了石河子绿洲3个地面站(炮台、石河子和莫索湾)41a(1964～2004年)的温度、相对湿度和降水,发现随着石河子垦区绿洲面积的扩大以及新疆增温增湿的气候变化趋势,石河子绿洲也有其比较独特的区域气候特征。主要表现在:(1)41a来温度、降水量呈增加趋势,但空气湿度比较稳定,没有明显的变化;(2)石河子绿洲的温度、降水量以及空气湿度具有明显的季节变化特征。秋季的温度变幅最大,冬季最小。这与新疆大部分地区冬季增温幅度最大有所不同。空气相对湿度变化的季节差异不明显。(3)以莫索湾站代表绿洲边缘,石河子站代表绿洲区,其降水、温度和空气湿度变化有明显差异,显示了比较明显的绿洲“冷岛”和“湿岛”效应。     

精细化MOS相对湿度预报方法研究

利用2003年5~9月MM5模式每隔1 h的站点基本要素预报场和物理量诊断场资料,以及相应时段内宁夏25个测站的相对湿度自记观测资料,同时采用多元线性和逐步回归2种MOS统计方法,预报宁夏25个测站5~9月48 h逐时相对湿度。对2004年夏季6~8月预报效果检验表明:MOS方法制作宁夏48 h逐时相对湿度预报结果是可用的或是可参考的;2种MOS统计方法预报结果相近,逐步回归方法比多元线性方法预报效果稍好,08:00预报误差明显低于20:00;当天气形势变化较平稳时,MOS预报结果稳定,平均绝对误差控制在10%左右;当有明显的变温等特殊天气时,误差变率起伏波动大,预报结果不稳定。     

WRF中土壤图及参数表的更新对华北夏季预报的影响研究

土壤质地及其物理性质的参数化对陆面过程模拟具有明显的影响。研究了土壤质地和土壤水文参数表的更新对WRF（Weather Research and Forecasting）模拟性能的影响。使用北京师范大学土壤属性数据集和修正后的土壤水文参数表替换WRF默认数据,对2017年6—8月华北地区开展数值模拟试验和评估验证。结果表明,模拟结果对土壤类型数据集和水文参数表的更新较为敏感,对地面要素预报有正效果。WRF默认土壤数据集中,中国东部以粘壤土为主,而在北京师范大学土壤数据集里则以壤土为主;修正后的土壤水文参数在Noah陆面过程中增强了裸土潜热蒸发能力。数值模拟试验表明,土壤输入数据和土壤水文参数的更新能够增强陆面向大气的潜热同时减弱感热输送,致使大气底层温度降低而湿度增大。利用华北区域748个地面气象观测站的2 m温度和2 m湿度对2017年夏季的模拟结果进行验证,结果显示更新试验对地面要素的预报偏差有较好的修正作用,能够将2 m温、湿度的预报技巧分别提高3.4%和2.9%。      

阜康日光温室内温湿度与外界气象要素的相关性分析

根据2011—2012年阜康六运温室内温湿度和附近气象站观测资料,利用相关和逐步回归分析,对不同天气状况下阜康地区日光温室内温湿度变化特征及其与外界气象要素的关系进行了研究。结果表明,不同天气状况下温室内的气温和空气相对湿度均有明显的日变化。影响日光温室内温湿度的主要因子为外界的日照时数、平均气温、最高气温、最低气温、空气湿度以及温室内前一天的温度和空气湿度等。建立了日光温室内外气象要素的相关模型,经拟合检验和应用检验,不同天气状况下日光温室内日平均气温、最高气温、最低气温、空气平均相对湿度、空气最小相对湿度的平均绝对误差分别在1.5℃、2.6℃、2.0℃、6.2%、9.6%以内,平均相对误差大部分在10%以内,具有较高的精度。确定了温室生火增温、移栽定植、通风及覆盖保温等气象服务指标。     

提高石油化工污泥脱水性能试验研究

本研究通过污泥比阻试验及叶片吸滤试验，探讨化学调理前后污泥的脱水性能。研制并开发了改善石油化工污泥脱水性能的最佳药剂ＰｔＣ，并为石油化工污泥脱水提供了必要的运行参数。     

Climatology of Cloud-base Height from Long-term Radiosonde Measurements in China

Clouds are critical to the global radiation budget and hydrological cycle, but knowledge is still poor concerning the observed climatology of cloud-base height(CBH) in China. Based on fine-resolution sounding observations from the China Radiosonde Network(CRN), the method used to estimate CBH was modified, and uncertainty analyses indicated that the CBH is good enough. The accuracy of CBH estimation is verified by the comparison between the sounding-derived CBHs and those estimated from the micro-pulse lidar and millimeter-wave cloud radar. As such, the CBH climatology was compiled for the period 2006–16. Overall, the CBH exhibits large geographic variability across China, at both 0800 Local Standard Time(LST) and 2000 LST, irrespective of season. In addition, the summertime cloud base tends to be elevated to higher altitudes in dry regions [i.e., Inner Mongolia and the North China Plain(NCP)]. By comparison, the Tibetan Plateau(TP),Pearl River Delta(PRD) and Sichuan Basin(SCB) have relatively low CBHs( 2.4 km above ground level). In terms of seasonality, the CBH reaches its maximum in summer and minimum in winter. A low cloud base tends to occur frequently( 70%) over the TP, PRD and SCB. In contrast, at most sites over the Yangtze River Delta(YRD) and the NCP, about half the cloud belongs to the high-cloud category. The CBH does not exhibit marked diurnal variation in summer, throughout all CRN sites, probably due to the persistent cloud coverage caused by the East Asia Summer Monsson. To the best of our knowledge, this is the first CBH climatology produced from sounding measurements in China, and provides a useful reference for obtaining observational cloud base information.     

多旋翼微型无人机气象探测适用性分析

本文通过多种试验对多旋翼无人机搭载微型气象探测设备进行低空温湿度探测和数据传输进行了适用性分析。结果表明,采用433 MHz无线透传的数据通讯方式数据传输稳定性较好,选用的微型气象探测设备温湿度传感器通过了实验室计量检定,与百叶箱温湿度的对比观测中二者一致性较好;无人机在单纯悬停过程中,温湿度观测与对比观测设备误差较小,相关性较好;在低空连续飞行过程中,温湿度观测结果与探空仪观测结果具有较好的一致性(温度平均绝对误差0.84℃,相对湿度平均绝对误差4%),但存在温湿度小脉动变化无法捕捉的情况,可能与设备温湿度响应时间、通风防辐射罩结果、飞行速度等有关。     

Seasonal Variation in Air Temperature and Relative Humidity on Building Areas and in Green Spaces in Beijing,China

The cooling and humidifying effects of urban parks are an essential component of city ecosystems in terms of regulating microclimates or mitigating urban heat islands(UHIs).Air temperature and relative humidity are two main factors of thermal environmental comfort and have a critical impact on the urban environmental quality of human settlements.We measured the 2-m height air temperature and relative humidity at the Beijing Olympic Park and a nearby building roof for more than 1 year to elucidate seasonal variations in air temperature and relative humidity,as well as to investigate the outdoor thermal comfort.The results showed that the lawn of the park could,on average,reduce the air temperature by(0.80±0.19)℃,and increase the relative humidity by(5.24±2.91)% relative to the values measured at the building roof during daytime.During the nighttime,the lawn of the park reduced the air temperature by(2.64±0.64)℃ and increased the relative humidity by(10.77±5.20)%.The park was cooler and more humid than surrounding building area,especially in night period(more pronounced cooling with 1.84℃).Additionally,the lawn of the park could improve outdoor thermal comfort through its cooling and humidifying effects.The level of thermal comfort in the park was higher than that around the building roof for a total of 11 days annually in which it was above one or more thermal comfort levels(average reduced human comfort index of 0.92)except during the winter.     

STUDY ON MIXED MODEL OF NEURAL NETWORK FOR FARMLAND FLOOD/DROUGHT PREDICTION*

The paper concerns a flood/drought prediction model involving the continuation of time series of a predictand and the physical factors influencing the change of predictand.Attempt is made to construct the model by the neural network scheme for the nonlinear mapping relation based on multi-input and single output.The model is found of steadily higher predictive accuracy by testing the output from one and multiple stepwise predictions against observations and comparing the results to those from a traditional statistical model.