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Surface-based microwave radiometer is used to measure the tropospheric parameter profiles continuously for 24 h. The measurement technology and retrieval methods are described clearly in this study. This paper focuses on the BP network and elaborates on it from a new perspective based on the Jacobian matrices between layers. Gradient descent is achieved by Jacobian matrices to train the network. A layered method is proposed to improve the efficiency and accuracy in training networks to obtain tropospheric water vapor and temperature profiles. Differently from the traditional method, the layered method divides the troposphere of 0–10 km into three layers based on the physical principles of cloud generation. Three networks, named as the bottom, the middle, and the upper network, are developed for the three layers. Therefore, three networks can be trained at the same time,using the same input and different output samples. According to the theories and the radiosonde data of 2012–2015 of Harbin China (45.46°N 126.40°E), a numerical experiment is designed to examine the layered method. The downwelling monochromatic radiative transfer model (MonoRTM) is used to calculate the atmospheric radiation brightness temperatures (BTs) with the radiosonde data. The experimental results show that the RMSEs of temperature and water vapor profiles of the layered method are reduced by 25.6% and 26.2%, respectively, at the altitude above 6 km, respectively, and the efficiency is improved by 20 times compared with the traditional method. [ABSTRACT FROM AUTHOR]

Accurate predictions of drought events to plan and manage the adverse effects of drought on agriculture and the environment requires tools that precisely predict standardized drought metrics. Improving on the World Meteorological Organization approved Standardized Precipitation Index (SPI), the multi-scalar Standardized Precipitation and Evapotranspiration Index ( SPE I), a variant of the SPI, is a relatively recent drought index, which takes into account the impacts of temperature change on overall dryness, along with precipitation and evapotranspiration effects. In this paper, an extreme learning machine (ELM) model was applied to predict SPE I in a drought-prone region in eastern Australia, and the quality of the model's performance was compared to that of a multiple linear regression (MLR), an artificial neural network (ANN), and a least support vector regression (LSSVR) model. The SPE I data were derived from climatic variables recorded at six weather stations between January 1915 and December 2012. Model performance was evaluated by means of the normalized root mean square error (NRMSE), normalized mean absolute error (NMAE), coefficients of determination (r 2 ), and the Nash-Sutcliffe efficiency coefficient (NASH) in the testing period. Results showed that the ELM and ANN models outperformed the MLR and LSSVR models, and all four models revealed a greater predictive accuracy for the 12-month compared to the 3-month SPE I predictions. For the 12-month SPE I predictions, optimal models had r 2 that ranged from 0.668 for the LSSVR model (Station 6) to 0.894 for the ANN model (Station 4). The good agreement between observed and predicted SPE I at different locations within the study region indicated the potential of the developed models to contribute to a more thorough understanding of potential future drought-risks in eastern Australia, and their applicability to drought assessments over multiple timescales. The models and findings have useful implications for water resources assessment in drought-prone regions. [ABSTRACT FROM AUTHOR]

In the first part of this paper, monthly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission 3B42 algorithm Version 7 (TRMM-3B42V7) are evaluated over Iran using the Generalized Three-Cornered Hat (GTCH) method which is self-sufficient of reference data as input. Climate Data Unit (CRU) is added to the GTCH evaluations as an independent gauge-based dataset thus, the minimum requirement of three datasets for the model is satisfied. To ensure consistency of all datasets, the two satellite products were aggregated to 0.5° spatial resolution, which is the minimum resolution of CRU. The results show that the PERSIANN-CDR has higher Signal to Noise Ratio (SNR) than TRMM-3B42V7 for the monthly rainfall estimation, especially in the northern half of the country. All datasets showed low SNR in the mountainous area of southwestern Iran, as well as the arid parts in the southeast region of the country. Additionally, in order to evaluate the efficacy of PERSIANN-CDR and TRMM-3B42V7 in capturing extreme daily-precipitation amounts, an in-situ rain-gauge dataset collected by the Islamic Republic of the Iran Meteorological Organization (IRIMO) was employed. Given the sparsity of the rain gauges, only 0.25° pixels containing three or more gauges were used for this evaluation. There were 228 such pixels where daily and extreme rainfall from PERSIANN-CDR and TRMM-3B42V7 could be compared. However, TRMM-3B42V7 overestimates most of the intensity indices (correlation coefficients; R between 0.7648–0.8311, Root Mean Square Error; RMSE between 3.29mm/day-21.2mm/5day); PERSIANN-CDR underestimates these extremes (R between 0.6349–0.7791 and RMSE between 3.59mm/day-30.56mm/5day). Both satellite products show higher correlation coefficients and lower RMSEs for the annual mean of consecutive dry spells than wet spells. The results show that TRMM-3B42V7 can capture the annual mean of the absolute indices (the number of wet days in which daily precipitation > 10 mm, 20 mm) better than PERSIANN-CDR. The results of daily evaluations show that the similarity of Empirical Cumulative Density Function (ECDF) of satellite products and IRIMO gauges daily precipitation, as well as dry spells with different thresholds in some selected pixels (include at least five gauges), are significant. The results also indicate that ECDFs become more significant when threshold increases. In terms of regional analyses, the higher SNR of the products on monthly (based on the GTCH method) and daily evaluations (significant ECDFs) is mostly consistent. [ABSTRACT FROM AUTHOR]

The forecasting of drought based on cumulative influence of rainfall, temperature and evaporation is greatly beneficial for mitigating adverse consequences on water-sensitive sectors such as agriculture, ecosystems, wildlife, tourism, recreation, crop health and hydrologic engineering. Predictive models of drought indices help in assessing water scarcity situations, drought identification and severity characterization. In this paper, we tested the feasibility of the Artificial Neural Network (ANN) as a data-driven model for predicting the monthly Standardized Precipitation and Evapotranspiration Index ( SPEI ) for eight candidate stations in eastern Australia using predictive variable data from 1915 to 2005 (training) and simulated data for the period 2006–2012. The predictive variables were: monthly rainfall totals, mean temperature, minimum temperature, maximum temperature and evapotranspiration, which were supplemented by large-scale climate indices (Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and Indian Ocean Dipole) and the Sea Surface Temperatures (Nino 3.0, 3.4 and 4.0). A total of 30 ANN models were developed with 3-layer ANN networks. To determine the best combination of learning algorithms, hidden transfer and output functions of the optimum model, the Levenberg–Marquardt and Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton backpropagation algorithms were utilized to train the network, tangent and logarithmic sigmoid equations used as the activation functions and the linear, logarithmic and tangent sigmoid equations used as the output function. The best ANN architecture had 18 input neurons, 43 hidden neurons and 1 output neuron, trained using the Levenberg–Marquardt learning algorithm using tangent sigmoid equation as the activation and output functions. An evaluation of the model performance based on statistical rules yielded time-averaged Coefficient of Determination, Root Mean Squared Error and the Mean Absolute Error ranging from 0.9945–0.9990, 0.0466–0.1117, and 0.0013–0.0130, respectively for individual stations. Also, the Willmott's Index of Agreement and the Nash–Sutcliffe Coefficient of Efficiency were between 0.932–0.959 and 0.977–0.998, respectively. When checked for the severity ( S ), duration ( D ) and peak intensity ( I ) of drought events determined from the simulated and observed SPEI , differences in drought parameters ranged from − 1.41–0.64%, − 2.17–1.92% and − 3.21–1.21%, respectively. Based on performance evaluation measures, we aver that the Artificial Neural Network model is a useful data-driven tool for forecasting monthly SPEI and its drought-related properties in the region of study. [ABSTRACT FROM AUTHOR]

Abstract: Microwave radiometers (MWRs) can be useful for the detection of mesoscale phenomena because they provide thermodynamic profiles in a minute time scale. These profiles are mainly used in non-precipitation conditions due to degraded accuracy of the MWR measurements in precipitation. Recently, Radiometrics Corporation used proprietary neural network methods to retrieve temperature, humidity and liquid profiles from off-zenith (15° elevation) radiometer observations to provide higher accuracy during precipitation. In this paper, using the MWR-retrieved temperature and humidity profiles with collocated radiosondes from June 2010 to September 2013 in Wuhan, the impact of precipitation on the MWR measurement accuracy as well as the effect of off-zenith neural network methods on it is investigated. In precipitation, the correlation coefficients of the MWR temperature and vapor density profiles against radiosondes are smaller than those in non-precipitation, and the bias and RMS against radiosondes also increase, especially around 2km heights. For the MWR relative humidity profile, the correlation coefficient in precipitation is obvious smaller than that in non-precipitation below 4.5km, and the bias and RMS against radiosondes are clearly larger above 5.5km. Moreover, the differences between the precipitation and non-precipitation cases mostly are statistically significant. Compared with the results of the zenith observation, the off-zenith observation makes a positive effect on reducing the impact of precipitation on the accuracy of MWR temperature and vapor density retrievals. On the whole, the MWR temperature bias and RMS against radiosondes in precipitation are reduced from 3.6 and 4.2K to 1.3 and 3.1K, respectively, and the MWR vapor density bias is also reduced from 1.10g/m3 to 0.18g/m3 with the RMS decreasing from 2.90g/m3 to 1.91g/m3. The temperature correlation coefficient between the MWR and radiosonde in precipitation is clearly improved above 3km heights, and the temperature bias and RMS are significantly reduced at most heights. For the MWR vapor density retrievals in precipitation, the correlation coefficient, bias and RMS against radiosondes are clearly improved above 2km heights. Additionally, the off-zenith observations during non-precipitation cases are also better compared to zenith observations. Therefore, off-zenith observations generally are better than zenith observations. This could be due to the fact that the off-zenith observations are more representative of the conditions in which radiosonde observations are also taken. [Copyright &y& Elsevier]