基于WACCM+DART的临近空间SABER和MLS温度观测资料同化试验

基于WACCM+DART（Whole Atmosphere Community Climate Model，Data Assimilation Research Test-Bed）临近空间资料同化预报系统，以2016年2月的一次平流层爆发性增温（SSW）事件为例，开展了临近空间SABER（Sounding of the Atmosphere using Broadband Emission Radiometry）和MLS（Microwave Limb Sounder）温度观测资料集合滤波同化试验.结果表明:同化SABER和MLS温度观测资料可显著降低WACCM模式在中间层和平流层中上部（0.001～10hPa）大气温度场的预报误差，改善CR试验在SSW发生时中间层变冷现象偏强、纬向风场首次发生反转的层次偏低以及增温恢复阶段0.1～10hPa的东风层提前消退、纬向风速偏大、平流层顶位置偏高等现象.基于ERA5（The Fifth Generation of ECMWF Reanalyses）再分析资料的检验表明:同化SABER和MLS温度资料明显有利于减小北半球高纬度地区（60°-90°N）平流层中上层和下中间层（0.1～14hPa）纬向风场以及平流层和中间层中下层（0.01～100hPa）温度场的分析误差；同化低层大气观测也有利于减小0.1～14hPa纬向风场和0.01～100hPa温度场的分析误差，但是不如同化SABER和MLS温度资料对临近空间纬向风场和温度场分析误差的改善效果显著.      

Forecasting space weather: Can new econometric methods improve accuracy?

Space weather forecasts are currently used in areas ranging from navigation and communication to electric power system operations. The relevant forecast horizons can range from as little as 24 h to several days. This paper analyzes the predictability of two major space weather measures using new time series methods, many of them derived from econometrics. The data sets are the A_p geomagnetic index and the solar radio flux at 10.7 cm. The methods tested include nonlinear regressions, neural networks, frequency domain algorithms, GARCH models (which utilize the residual variance), state transition models, and models that combine elements of several techniques. While combined models are complex, they can be programmed using modern statistical software. The data frequency is daily, and forecasting experiments are run over horizons ranging from 1 to 7 days. Two major conclusions stand out. First, the frequency domain method forecasts the A_p index more accurately than any time domain model, including both regressions and neural networks. This finding is very robust, and holds for all forecast horizons. Combining the frequency domain method with other techniques yields a further small improvement in accuracy. Second, the neural network forecasts the solar flux more accurately than any other method, although at short horizons (2 days or less) the regression and net yield similar results. The neural net does best when it includes measures of the long-term component in the data.     

Forecast of recurrent geomagnetic storms

Long-term forecast of space weather allows in achieving a longer lead time for taking the necessary precautions against disturbances. Hence, there is a need for long-term forecasting of space weather. We studied the possibility for a long-term forecast of recurrent geomagnetic storms. Geomagnetic storms recur with an approximate 27-day period during the declining phase of a solar cycle. These disturbances are caused by the passage of corotating interaction regions, which are formed by interactions between the background slow-speed solar wind and high-speed solar wind streams from a coronal hole. In this study, we report on the performance of 27-day-ahead forecasts of the recurrent geomagnetic disturbances using Kp index. The methods of the forecasts are on the basis of persistence, autoregressive model, and categorical forecast using occurrence probability. The forecasts show better performance during the declining phase of a solar cycle than other phases. The categorical forecast shows the probability of detection (POD) more than 0.5 during the declining phase. Transition of the performance occurs sharply among the declining phases and other phases.     

Predicting Earth orientation changes from global forecasts of atmosphere-hydrosphere dynamics

Effective Angular Momentum (EAM) functions obtained from global numerical simulations of atmosphere, ocean, and land surface dynamics are routinely processed by the Earth System Modelling group at Deutsches GeoForschungsZentrum. EAM functions are available since January 1976 with up to 3?h temporal resolution. Additionally, 6?days-long EAM forecasts are routinely published every day. Based on hindcast experiments with 305 individual predictions distributed over 15?months, we demonstrate that EAM forecasts improve the prediction accuracy of the Earth Orientation Parameters at all forecast horizons between 1 and 6?days. At day 6, prediction accuracy improves down to 1.76 mas for the terrestrial pole offset, and 2.6 mas for $\mathrm{\Delta }$UT1, which correspond to an accuracy increase of about 41% over predictions published in Bulletin A by the International Earth Rotation and Reference System Service.     

TIEGCM集合卡尔曼滤波同化模型设计及初步试验

选择参数化的电离层热层理论模型TIEGCM作为背景模型,基于COSMIC掩星观测的电子密度廓线数据,应用集合卡尔曼滤波方法建立全球电离层电子密度同化模型,实现了全球电离层的电子密度同化.同化结果表明,该同化模型能将观测资料有效同化到背景模式中,获得全球三维电离层电子密度.与背景模式相比,同化得到的电子密度相对于观测值的偏差显著下降.对于有同化和无同化参与的试验,N_mF₂的标准偏差分别降低约60%和20%.此外,分组同化与同时同化的结果对比显示,平均偏差改善基本一致,同时同化后的标准偏差在峰值高度以上略有减小.      

Rapid local ionosphere modeling based on Precise Point Positioning over Iran: A case study under 2014 solar maximum

Presently, the ionosphere effect is the main source of the error in the Global Positioning System (GPS) observations. This effect can largely be removed by using the two-frequency measurements, while to obtain the reasonable results in the single-frequency applications, an accurate ionosphere model is required. Since the global ionosphere models do not meet our needs everywhere, the local ionosphere models are developed. In this paper, a rapid local ionosphere model over Iran is presented. For this purpose, the GPS observations obtained from 40 GPS stations of the Iranian Permanent GPS Network (IPGN) and 16 other GPS stations around Iran have been used. The observations have been selected under 2014 solar maximum, from the days 058, 107, 188 and 271 of the year 2014 with different geomagnetic activities. Moreover, ionospheric observables based on the precise point positioning (PPP) have been applied to model the ionosphere. To represent our ionosphere model, the B-spline basis functions have been employed and the variance component estimation (VCE) method has been used to regularize the problem.To show the efficiency our PPP-derived local ionosphere model with respect to the International GNSS Service (IGS) global models, these models are applied on the single point positioning using single-frequency observations and their results are compared with the precise coordinates obtained from the double-differenced solution using dual-frequency observations. The results show that the 95th percentile of horizontal and vertical positioning errors of the single-frequency point positioning are about 3.1 and 13.6?m, respectively, when any ionosphere model are not applied. These values significantly improve when the ionosphere models are applied in the solutions. Applying CODE’s Rapid Global ionosphere map (CORG), improvements of 59% and 81% in horizontal and vertical components are observed. These values for the IGS Global ionosphere map (IGSG) are 70% and 82%, respectively. The best results are obtained from our local ionosphere model, where 84% and 87% improvements in horizontal and vertical components are observed. These results confirm the efficiency of our local ionosphere model over Iran with respect to the global models. As a by-product, the Differential Code Biases (DCBs) of the receivers are also estimated. In this line, we found that the intra-day variations of the receiver DCBs could be significant. Therefore, these variations must be taken into account for the precise ionosphere modeling.     

Evaluation of three high-resolution satellite precipitation estimates: Potential for monsoon monitoring over Pakistan

Multi-sensor precipitation datasets including two products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and estimates from Climate Prediction Center Morphing Technique (CMORPH) product were quantitatively evaluated to study the monsoon variability over Pakistan. Several statistical and graphical techniques are applied to illustrate the nonconformity of the three satellite products from the gauge observations. During the monsoon season (JAS), the three satellite precipitation products captures the intense precipitation well, all showing high correlation for high rain rates (>30 mm/day). The spatial and temporal satellite rainfall error variability shows a significant geo-topography dependent distribution, as all the three products overestimate over mountain ranges in the north and coastal region in the south parts of Indus basin. The TMPA-RT product tends to overestimate light rain rates (approximately 100%) and the bias is low for high rain rates (about ±20%). In general, daily comparisons from 2005 to 2010 show the best agreement between the TMPA-V7 research product and gauge observations with correlation coefficient values ranging from moderate (0.4) to high (0.8) over the spatial domain of Pakistan. The seasonal variation of rainfall frequency has large biases (100–140%) over high latitudes (36N) with complex terrain for daily, monsoon, and pre-monsoon comparisons. Relatively low uncertainties and errors (Bias ±25% and MAE 1–10 mm) were associated with the TMPA-RT product during the monsoon-dominated region (32–35N), thus demonstrating their potential use for developing an operational hydrological application of the satellite-based near real-time products in Pakistan for flood monitoring.     

Evaluating the predictability of eight Atmospheric-Oceanic signals affecting Iran’s Droughts,employing intelligence based and stochastic methods

The large-scale atmospheric-oceanic phenomena are among the main effective factors in the droughts in the Middle East, especially in Iran. Since these effects are usually delayed, their relevant signals can be useful for predicting droughts. As a result, the provision of a precise prediction of these signals can be efficient in increasing the drought prediction prospect. The current study predicts 8 cases of the most effective oceanic signals on the droughts which have been investigated in Iran. To do so, the problem-solving method with the time series prediction approach is based on the two model types intelligence-based (including multilayer perceptron [MLP] and support vector machine [SVM]) and stochastic (including Autoregressive Integrated Moving Average [ARIMA]) has been used. The model's input for each index included the time lags of the same index itself, which was determined by the autocorrelation function. Based on the evaluation criteria, the results were indicative of the weak predictability of the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO), while the Extreme Eastern Tropical Pacific sea surface temperature (Niño [1 + 2]), East Central Tropical Pacific sea surface temperature (Niño [3 + 4]), and Oceanic Niño Index (ONI) were predicted with very good accuracy, and there is a high overlap between their predictions and observations (95.9 % < R² < 99.3 %). In the extreme events also, the rate of normalized forecasting error for Niño (1 + 2), Niño (3 + 4), and ONI were in the medium (20–30 %), good (10–20 %), and excellent (0–10 %) ranges, respectively. The comparison between the models also indicates a partial superiority of the ARIMA stochastic model over the SVM and MLP models. The overall results of the study are indicative of the applicability of the predictions of the three mentioned indices as the inputs to increase precipitation and drought forecasting prospects in Iran (as well as all regions affected by them); which have the research value for further studies in terms of drought forecasting.     

Characterisation of residual ionospheric errors in bending angles using GNSS RO end-to-end simulations

Global Navigation Satellite System (GNSS) radio occultation (RO) is an innovative meteorological remote sensing technique for measuring atmospheric parameters such as refractivity, temperature, water vapour and pressure for the improvement of numerical weather prediction (NWP) and global climate monitoring (GCM). GNSS RO has many unique characteristics including global coverage, long-term stability of observations, as well as high accuracy and high vertical resolution of the derived atmospheric profiles. One of the main error sources in GNSS RO observations that significantly affect the accuracy of the derived atmospheric parameters in the stratosphere is the ionospheric error. In order to mitigate the effect of this error, the linear ionospheric correction approach for dual-frequency GNSS RO observations is commonly used. However, the residual ionospheric errors (RIEs) can be still significant, especially when large ionospheric disturbances occur and prevail such as during the periods of active space weather. In this study, the RIEs were investigated under different local time, propagation direction and solar activity conditions and their effects on RO bending angles are characterised using end-to-end simulations. A three-step simulation study was designed to investigate the characteristics of the RIEs through comparing the bending angles with and without the effects of the RIEs. This research forms an important step forward in improving the accuracy of the atmospheric profiles derived from the GNSS RO technique.     

A novel predictive algorithm for double difference observations of obstructed BeiDou geostationary earth orbit (GEO) satellites

Transmission link disturbances and device failure cause global navigation satellite system (GNSS) receivers to miss observations, leading to poor accuracy in real-time kinematic (RTK) positioning. Previously described solutions for this problem are influenced by the length of the prediction period, or are unable to account for changes in receiver state because they use information from previous epochs to make predictions. We propose an algorithm for predicting double difference (DD) observations of obstructed BeiDou navigation system (BDS) GEO satellites. Our approach adopts the first-degree polynomial function for predicting missing observations. We introduce a Douglas-Peucker algorithm to judge the state of the rover receiver to reduce the impact of predictive biases. Static and kinematic experiments were carried out on BDS observations to evaluate the proposed algorithm. The results of our navigation experiment demonstrate that RTK positioning accuracy is improved from meter to decimeter level with fixed ambiguity (horizontal?<?2?cm, vertical?<?18?cm). Horizontal accuracy is improved by over 50%, and the vertical accuracies of the results of the static and kinematic experiments are increased by 47% and 27% respectively, compared with the results produced by the classical approach. Though as the baseline becomes longer, the accuracy is weakened, our predictive algorithm is an improvement over existing approaches to overcome the issue of missing data.     

A decade of balloon observations of auroral x-rays

Balloon observations of bremsstrahlung x-rays carried out by The University of Calgary over the past decade deal with morphological studies of auroral electron precipitation. The program attempts to deal with the understanding of correlation between parent electrons and secondary x-rays, study of microbursts, East-West and North-South extent of electron precipitation, and precipitation during pulsating aurora. Although the overall program involves the use of both rocket and balloon-borne payloads, here we present only the results of the balloon experiments.     

基于电子密度同化的热层大气密度预报方法

采用热层电离层耦合模式TIEGCM和集合卡尔曼滤波同化方法,利用同化COSMIC电离层掩星电子密度数据优化热层电离层参量,并将模式预报的大气密度与CHAMP卫星大气密度数据进行对比,分别开展模拟和实测数据的同化预报实验.在模拟数据同化实验中,状态向量包含温度、风场和离子成分的实验结果表明,仅优化温度即可达到最优的热层大气密度预报效果.在实测数据同化实验中,将温度作为状态向量参数,优化结果表明,循环同化过程中模式预报的大气密度相对偏差的均方根误差在48h内从38%减小到27%,同化稳定时间至少需要30h.预报过程中大气密度预报效果的改善持续时间为34h.这表明电子密度同化能够改善热层大气密度的预报精度,设计的实验方案合理可行,可获得较长的预报时效.      

Meteorological Research Institute multivariate ocean variational estimation (MOVE) system: Some early results

The Meteorological Research Institute multivariate ocean variational estimation (MOVE) System has been developed as the next-generation ocean data assimilation system in Japan Meteorological Agency. A multivariate three-dimensional variational (3DVAR) analysis scheme with vertical coupled temperature–salinity empirical orthogonal function modes is adopted. The MOVE system has two varieties, the global (MOVE-G) and North Pacific (MOVE-NP) systems. The equatorial Pacific and western North Pacific are analyzed with assimilation experiments using MOVE-G and -NP, respectively. In each system, the salinity and velocity fields are well reproduced, even in cases without salinity data. Changes in surface and subsurface zonal currents during the 1997/98 El Niño event are captured well, and their transports are reasonably consistent with in situ observations. For example, the eastward transport in the upper layer around the equator has 70 Sv in spring 1997 and weakens in spring 1998. With MOVE-NP, the Kuroshio transport has 25 Sv in the East China Sea, and 40 Sv crossing the ASUKA (Affiliated Surveys of the Kuroshio off Cape Ashizuri) line south of Japan. The variations in the Kuroshio transports crossing the ASUKA line agree well with observations. The Ryukyu Current System has a transport ranging from 6 Sv east of Taiwan to 17 Sv east of Amami. The Oyashio transport crossing the OICE (Oyashio Intensive observation line off Cape Erimo) line south of Hokkaido has 14 Sv southwestward (near shore) and 11 Sv northeastward (offshore). In the Kuroshio–Oyashio transition area east of Japan, the eastward transport has 41 Sv (32–36°N) and 12 Sv (36–39°N) crossing the 145°E line.     

基于背景和变化稀疏性的瞬变源图像重建算法

针对射电干涉测量通过结合长时间观测的多组可见度数据对静态源进行高空间分辨率成像，但无法得到时变信息的问题，提出了一种基于背景和目标变化的直和空间上稀疏性的稀疏基线综合孔径动态场景图像重建算法。将起始时刻的亮温和相邻时刻的亮温变化作为待求解矢量，不同时刻的亮温表示为它们的和，构造测量方程，并利用起始时刻亮温和相邻时刻亮温的稀疏性进行求解，重建出不同时刻的瞬变源图像。仿真实验结果表明，对于小区域背景下的瞬变源，所提算法与已有方法相当，对于大区域背景下的瞬变源，所提算法优于已有方法。      

A superposed epoch study of the effects of solar wind stream interface events on the upper mesospheric and lower thermospheric temperature

The response of mesosphere and lower thermosphere (MLT) temperature to energetic particle precipitation over the Earth’s polar regions is not uniform due to complex phenomena within the MLT environment. Nevertheless, the modification of MLT temperatures may require an event-based study to be better observed. This work examines the influence of precipitation, triggered by solar wind stream interfaces (SI) event from 2002 to 2007, on polar MLT temperature. We first test the relationship between the ionospheric absorption measured by the SANAE IV (South African National Antarctic Expedition IV) riometer and the layer of energetic particle precipitation from POES (Polar Orbiting Environmental Satellites). The combined particle measurements from POES 15, 16, 17 and 18 were obtained close in time to the pass of the SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) temperature retrieval. Here, a superposed epoch technique is described and implemented to obtain average temperature profiles during SI-triggered particle precipitation. The superposed epoch average shows no significant temperature decrease below 100 km prior to the onset of SI-triggered precipitation, whereas a clear superposed average temperature decrease is observed at 95 km after the SI impact. A case study of SI event also yields similar observations. Results indicate that cooling effects due to the production of mesospheric odd hydrogen might be major contributors to temperature decrease under compressed solar wind stream.     

三层月壤模型的多通道微波辐射模拟与月壤厚度的反演

由月球表面数字高程试验性地构造了整个月球表面月壤厚度的分布.根据Clementine探月卫星的紫外-可见光光学数据,计算了整个月球表面月壤中FeO+TiO2含量分布,给出了整个月球表面月壤介电常数分布.由月球表层温度的观测结果以及月壤的导热特性,给出了月尘层与月壤层温度随纬度分布的经验公式.在这些条件的基础上,建立了月尘、月壤、月岩三层微波热辐射模型.由起伏逸散定理,模拟计算了该月球模型多通道辐射亮度温度.然后,以此辐射亮度温度模拟加随机噪声为理论观测值,按三层模型提出了月壤层厚度反演方法.由于高频通道穿透深度小,由高频通道的辐射亮度温度按照两层月尘-月壤微波热辐射模型反演月尘层与月壤层的物理温度,再由穿透深度较大的低频通道辐射亮度温度反演月壤层厚度.对于反演的相对误差也进行了讨论.      

Modelling of air temperature using remote sensing and artificial neural network in Turkey

The aim of this research was to forecast monthly mean air temperature based on remote sensing and artificial neural network (ANN) data by using twenty cities over Turkey. ANN contained an input layer, hidden layer and an output layer. While city, month, altitude, latitude, longitude, monthly mean land surface temperatures were chosen as inputs, and monthly mean air temperature was chosen as output for network. Levenberg–Marquardt (LM) learning algorithms and tansig, logsig and linear transfer functions were used in the network. The data of Turkish State Meteorological Service (TSMS) and Technological Research Council of Turkey–Bilten for the period from 1995 to 2004 were chosen as training when the data of 2005 year were being used as test. Result of research was evaluated according to statistical rules. The best linear correlation coefficient (R), and root mean squared error (RMSE) between the estimated and measured values for monthly mean air temperature with ANN and remote sensing method were found to be 0.991–1.254 K, respectively.     

基于MAX452的压力传感器温度补偿研究

设计了一套基于MAX1452的压力变送和温度补偿系统。通过建立数学模型，分析了温度漂移的原因和补偿机理。设计硬件电路，并编制软件系统。通过试验验证了数学模型和补偿机理的正确性。试验结果显示，传感器补偿后全温度范围内误差相较于补偿前提高了一个数量级。     

Using the radial basis function neural network to predict ionospheric critical frequency of F2 layer over Wuhan

Neural networks (NNs) have been applied to ionospheric predictions recently. This paper uses radial basis function neural network (RBF-NN) to forecast hourly values of the ionospheric F₂ layer critical frequency(f_oF₂), over Wuhan (30.5N, 114.3E), China. The false nearest neighbor method is used to determine the embedding dimension, and the principal component analysis (PCA) is used to reduce noise and dimension. The whole study is based on a sample of about 26,000 observations of f_oF₂ with 1-h time resolution, derived during the period from January 1981 to December 1983. The performance of RBF-NN is estimated by calculating the normalized root-mean-squared (NRMSE) error, and its results show that short-term predictions of f_oF₂ are improved.     

Tropopause analysis over the Iranian region using GPS radio occultation data

Near-tropopause phenomena like upper level fronts and cyclones, penetrative cumulus convection and mesoscale mechanisms of exchange make important contributions to the mixing processes in the atmosphere. Spatio-temporal monitoring of the tropopause height, temperature and pressure is an appropriate tool to show the running processes in the atmosphere. In this study, GPS radio occultation data is used to investigate the tropopause height fluctuations and the relation between the stratosphere–troposphere exchange and the aforementioned phenomena over the Iranian region. The paper shows how the position of the sub-tropical jet has changed with time, using GPS radio occultation observations. The tropopause height changes latitudinally, and three different bimodal probability distribution functions are observed. The results also show that the mixing region in the south of Iran is associated with the subtropical jet in winter. However, this region shifts north of Iran due to changes in the position of the subtropical jet during the summer. Consistency of the mixing region from the radio occultation data and the total ozone of TOMS over the Iranian region is also observed.