Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.     

Latitudinal and Scan-dependent Biases of Microwave Humidity Sounder Measurements and Their Dependences on Cloud Ice Water Path

The relationship between differences in microwave humidity sounder(MHS)–channel biases which represent measured brightness temperatures and model-simulated brightness temperatures, and cloud ice water path(IWP) as well as the influence of the cloud liquid water path(LWP) on the relationship is examined. Seven years(2011–17) of NOAA-18 MHS-derived measured brightness temperatures and IWP/LWP data generated by the NOAA Comprehensive Large Array-data Stewardship System Microwave Surface and Precipitation Products System are used. The Community Radiative Transfer Model, version2.2.4, is used to simulate model-simulated brightness temperatures using European Center for Medium-Range Weather Forecasts reanalysis data as background fields. Scan-angle deviations of the MHS window channel biases range from-1.7 K to1.0 K. The relationships between channels 2, 4, and 5 biases and scan angle are symmetrical about the nadir. The latitudedependent deviations of MHS window channel biases are positive and range from 0–7 K. For MHS non-window channels,the latitudinal deviations between measured brightness temperatures and model-simulated brightness temperatures are larger when the detection height is higher. No systematic warm or cold deviations are found in the global spatial distribution of difference between measured brightness temperatures and model-simulated brightness temperatures over oceans after removing scan-angle and latitudinal deviations. The corrected biases of five different MHS channels decrease differently with respect to the increase in IWP. This decrease is stronger when LWP values are higher.     

BDS卫星差分码偏差稳定性分析及其短期预报

利用MGEX(multi-GNSS experiment)发布的BDS卫星差分码偏差(differential code bias, DCB)产品，比较分析不同太阳活动水平下BDS卫星DCB产品的稳定性变化特性，并采用差分自回归移动平均(auto-regressive integrated moving average, ARIMA)时间序列预测模型，实现不同太阳活动水平下BDS卫星DCB的短期预报。结果表明，在太阳活动高年，BDS卫星DCB日解值稳定度、月稳定性均明显低于太阳活动低年，且不同卫星星座类型的BDS卫星DCB稳定性也存在差异；ARIMA时间序列预报结果与MGEX发布值符合程度较好，优于多项式拟合法预测结果。     

一种顾及GNSS系统间偏差的伪距单点定位方法

对系统间偏差的成分和影响因素进行分析,发现各项改正后残余误差对系统间偏差估值的影响可归结为与卫星编号和广播星历更新相关。提出一种新的顾及参与解算卫星构成的系统间偏差估计方案,新方案能够在仅有4颗多模GNSS卫星可见时提供可靠的位置服务。     

BDS/GPS卫星数据质量分析软件开发及应用研究

通过对TEQC数据质量模块相关算法进行分析,实现了支持RINEX2、3 格式的BDS/GPS卫星数据质量分析软件DataQC的开发,其主要参数包括数据完整率、多路径指数、信噪比和周跳等,并增加卫星分布状况以及电离层、接收机硬件延迟偏差估计功能。通过多组数据验证,软件计算指标与TEQC结果具有较高的一致性;在电离层估计方面,与全球电离层格网（global ionosphere maps, GIM）插值计算结果的RMS在3 TECu之内,并可用于接收机硬件延迟偏差稳定性的分析。     

中国区域GPS站海潮负荷形变特征分析

讨论5个全球海洋潮汐模型NAO99b、FES2004、GOT4.7、TPXO7.2和EOT11a在中国区域的差异性和适用性,并基于这些全球海潮模型和高分辨率区域海潮模型,分析中国区域GPS站海潮负荷形变特征。采用GIPSY软件对中国区域GPS站长时间观测数据进行处理,分析高精度区域海洋潮汐模型对中国沿海GPS站高精度位置时间序列的影响。分析表明,加入区域海洋潮汐负荷形变后,对GPS时间序列垂向的影响最大可达5.01 mm,可能会引起GPS时间序列中包含15 d和173 d 的虚假周期信号。     

遥感影像内容的语义查询算法与应用

海量遥感影像数据如何进行方便快捷的查询检索与定位,是影像数据存储管理部门所面临的主要问题,同时也是制约影像数据为用户提供更好服务的瓶颈。本文主要分析了当前遥感影像数据库查询检索的几种方式和它们的利弊,进而提出一种在近乎海量的遥感数据中简单、方便、快捷的算法与实现思想。并在已有的影像数据库引擎原型系统上,设计出一种检索查询遥感影像内地物的方法。这种方法以矢量要素的空间信息和属性信息为引导,快速准确的定位地物所在的影像块,为遥感影像的有效管理和应用提供了一种可行的方法。     

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PGIS支持下的警务信息地图位置采集方式探讨

随着PGIS平台在全国公安机关的广泛推广和深入应用,迫切需要将已有MIS业务系统与PGIS实现整合,而其中最为重要的就是如何实现基于PGIS的警务业务信息空间化问题.本文即以南京市公安局相关信息系统为例,对PGIS支持下的警务信息空间位置采集方式进行了探讨.对警务业务系统中相关信息的空间位置采集方式进行了流程设计、数据...     

卫星钟差采样间隔对高频PPP-ZTD精度的影响

高频对流层延迟（ZTD）的提取对于反映水汽含量的高时间分辨率瞬时变化及其在暴雨短临预报中的应用具有重要意义。基于精密单点定位技术（PPP）分析了不同采样间隔的卫星钟差对PPP-ZTD精度的影响。结果表明,卫星钟差的时间间隔小于30 s时,所获得的PPP-ZTD(RMS<4 mm)比5 min间隔的（RMS<6 mm）精度要高;而5 s与30 s采样间隔的卫星钟差所获得的ZTD精度相当。