Categorical Statistics Research Articles

Ideal Character of Muslim Generation of Industrial Revolution Era 4.0 and Society 5.0

This article was to find out the character Muslim prioritizes in facing the Industrial Revolution Era 4.0 and Society 5.0 The data analysis technique in this study uses categorical statistics from the distribution of questionnaires in the Bandung area and it’s surroundings. The results of the distribution of the questionnaire showed the ideal character sequence in Muslim children, namely honesty, discipline, responsibility, polite, confident, hardworking, tolerant, creative and innovative, caring, productive and religious. Then, it can be concluded that these characters can be the foundation of Muslim children facing the development of the industrial revolution era 4.0 and society 5.0.
 Keywords: Ideal Character, Muslim Generation, Industrial Revolution 4.0

Transformational Adaptation in Least Developed Countries: Does Expanded Stakeholder Participation Make a Difference?

Did the novel planning arrangements in the National Adaptation Programmes of Action (NAPAs) enable stakeholders to substantively influence adaptation planning? If so, does the observed influence have potential for more transformational adaptation? We inform these questions by reviewing and coding the first 50 NAPAs, prepared by the world’s poorest nations with support from the United Nations Framework Convention on Climate Change (UNFCCC). We then apply categorical statistics and qualitative comparative analysis to test for stakeholder influence on the planning process and outcomes. We find little evidence that the composition of stakeholder participation influenced climate vulnerability analysis or adaptation planning in the NAPAs. Although the NAPAs were designed to be participatory and country-driven, they were constrained by limited budgets, prescribed guidelines from the UNFCCC, and the challenges of cultivating effective stakeholder participation. Key aspects of NAPAs even worked against generating transformational adaptation. Chief amongst these, risk exposure and sensitivity were emphasized over adaptive capacity in assessing vulnerability, and cost- effectiveness and synergies with existing development and environmental policies were priorities for selecting adaptation actions. These barriers to effective stakeholder engagement and transformational adaptation are timely reminders for those countries currently in the process of preparing their National Adaptation Plans to the UNFCCC.

Real-time 7-day forecast of pollen counts using a deep convolutional neural network

Several studies have used regression analyses to forecast pollen concentrations, yet few have applied a deep neural network in their research. This study implements a deep convolutional neural network with the great potential to recognize patterns of pollen phenomena that enable the prediction of pollen concentrations. We train the model using data from 2009 to 2015 from multiple meteorological datasets, satellite data and processed data reflecting pollen flux as input for the model. The model forecasts pollen counts 1–7 days ahead for the entire year of 2016. Comparison of daily forecasts to observations, the algorithm obtains a relatively high index of agreement and Pearson correlation coefficient of up to 0.90 and 0.88, respectively. An evaluation of categorical statistics based on defined threshold levels shows satisfactory results. Critical success index of the model forecasts is as high as 0.887 for weed pollen, 0.646 for tree pollen, and 0.294 for grass pollen. Forecasts of grass pollen exhibit the largest decrease in accuracy because of the strong variance in annual pollen concentrations. Forecasts of weed pollen exhibit the greatest consistency, with a 7-day forecast correlation and index of agreement of 0.82 and 0.77, respectively, during the peak season. This correlates with the consistency of annual and seasonal trends of weed pollen within the study area. Compared to the conventional modeling approaches, convolutional neural network shows a promising ability to predict pollen for multiple days to allow individuals with allergies to take proper precautions during high pollen days.

Renovation in hospitals: Training construction crews to work in health care facilities

Health care facilities require frequent renovations to maintain or enhance their service, and to meet the dynamic demands of their patients. Construction activities in active health care facilities are a significant contributor to various challenges that range from infection to death. It is therefore essential to minimize the adverse impacts of construction activities on health care units as well as their adjacent sites. A questionnaire was developed to study current training modules to prepare construction crews to work in health care environments. The survey was disseminated among professionals of the top 15 health care contractors. A total of 129 individuals participated, and their responses were analyzed using descriptive and categorical statistics. This study investigates current training practices regarding (1) the level of training, (2) the frequency of training, and (3) the impact that the sensitivity of the project has on the training. To effectively prepare construction crews, special training must be provided to them. There are uncertainties about the sufficiency and impact of the existing training. Existing trainings are tailored for upper management positions, and the amount/frequency of training for construction crews are substantially low. Findings of this study contribute to characterizing the activities and conditions pertaining to training of construction crews.

Evaluation of satellite-based precipitation estimates over Algeria during 1998–2016

This study concentrated on the assessment of two operational high-resolution satellite-based rainfall products, Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and NOAA CPC Morphing Technique (CMORPH) over Algeria. The assessment was carried out for a duration of 19-years ranging between 1998 and 2016 using 20 rain gauge datasets. The study area can be divided into five regions (zones) according to Köppen climate classification (CSA, BSK, BWK, BSH, and BWH). Both satellite derived rainfall estimates (SRE); (CHIRPS and CMORPH) were evaluated over these regions. Continuous statistics was employed to measure their performance in evaluating and reproducing rainfall while categorical statistics employed on daily time scale to assess the ability of SRE in detecting rain/no rain events. Correlation Coefficient (R), Bias and Root Mean Square Error (RMSE) are employed on daily, monthly as well as on annual timescales. At daily scale, most SRE showed unsatisfactorily performance over all the five regions with exception for CMORPH products exhibiting better performance. However, as the time step augmented, the performance of SRE enhanced. At monthly time scale a higher agreement was observed. The CHIRPS performed the best (R = 0.9), whereas CMORPH-CRTv1.0 showed relatively weaker but good correlation (R = 0.83). At annually time scale CHIRPS has a better performance in CSA, BSK regions, on the other hand CMORPH demonstrate a better results BSH, BWK, BWH regions. The study demonstrate that reliable evaluation of rainfall in Algeria with different climate zones employing satellite-based precipitation estimation products remains a challenge.

Evaluation of season long rainfall simulated by WRF over the NWH region: KF vs. MSKF

Abstract This study is aimed to assess the performance of Numerical Weather Predication (NWP) model Weather Research and Forecasting (WRF) in simulating the season long rainfall pattern over the North-West Himalayan (NWH) region. For this purpose, two experiments are designed with identical initial conditions but varying cumulus physics options viz.; Kain-Fritsch (KF) and Multi-scale Kain-Fritsch (MSKF). WRF simulations have been carried out for multiple nested domains (45, 15 and 5 km) configuration for monsoon season of 2017. Model derived rainfall corresponding to two experiments has been evaluated against the gridded rainfall data prepared by India Meteorological Department (IMD) and satellite based GPM-IMERGE and INSAT-3D HE-rain. Present results unveiled that the KF scheme overestimates the amount of rainfall over the entire study domain; whereas, MSKF scheme is capable to simulate the magnitude of rainfall comparable to the observations. Model simulated rainfall corresponding to both the experiments yields stronger correlation with satellite based GPM-IMERG as compared to the IMD data. It is noted based on statistically robust Chow test that the WRF simulation with KF scheme estimates break point in the rainfall pattern at 1200 m whereas WRF simulation with MSKF scheme resolves break point at 2000 m which is in close agreement with the previous studies. Categorical statistics metrics and qualitative analysis of WRF simulations suggest that the scale aware cumulus scheme MSKF performs better in resolving monsoon season rainfall pattern over the rugged topography of the NWH region. Present study has implications for improved understanding of spatial pattern of monsoon rainfall over the NWH region.

Dissecting Performances of PERSIANN-CDR Precipitation Product over Huai River Basin, China

Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product.

Individual birth planning as a teaching-learning strategy for good practices in obstetric care.

To verify students' knowledge about individual birth planning and learn their opinion about the use of this teaching-learning strategy for good obstetric practices. This is a descriptive study conducted with midwifery students who made primary health care internships. Data collection was performed digitally from 01/2017 to 05/2017 through the Google Forms®Platform. The analysis was performed by descriptive categorization statistics of statements. The study followed the required ethical standards. The form was sent to 97 students and answered by 40% of them. All respondents reported knowing the birth planning, and 87% applied it during prenatal care. The most frequent (45%) points about the birth plan were those that promoted women's empowerment and autonomy. The most cited methodological suggestions for its application were to focus on content (76%) and increase the number of meetings (50%). Individual birth planning was recognized by 79% of the participants as an important teaching strategy. In addition to knowing birth planning and applying it, the students consider it very relevant for teaching and learning good obstetrical practices.

Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998–2013)

Knowledge and studies on precipitation in the Amazon Basin (AB) are determinant for environmental aspects such as hydrology, ecology, as well as for social aspects like agriculture, food security, or health issues. Availability of rainfall data at high spatio-temporal resolution is thus crucial for these purposes. Remote sensing techniques provide extensive spatial coverage compared to ground-based rainfall data but it is imperative to assess the quality of the estimates. Previous studies underline at regional scale in the AB, and for some years, the efficiency of the Tropical Rainfall Measurement Mission (TRMM) 3B42 Version 7 (V7) (hereafter 3B42) daily product data, to provide a good view of the rainfall time variability which is important to understand the impacts of El Nino Southern Oscilation. Then our study aims to enhance the knowledge about the quality of this product on the entire AB and provide a useful understanding about his capacity to reproduce the annual rainfall regimes. For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013, with the aim to know whether 3B42 is reliable for climate studies. Analysis of quantitative (Bias, Relative RMSE) and categorical statistics (POD, FAR) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions. In the latter, the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation. In general, a good agreement is observed between rain gauge derived regimes and those from 3B42; however, performance is better in the rainy period. Finally, an original way to validate the estimations is by taking into account the interannual variability of rainfall regimes (i.e., the presence of sub-regimes): four sub-regimes in the northeast AB defined from rain gauges and 3B42 were found to be in good agreement. Furthermore, this work examined whether TRMM 3B42 V7 rainfall estimates for all the grid points in the AB, outgoing longwave radiation (OLR) and water vapor flux patterns are consistent in the northeast of AB.

SPATIAL ESTIMATION OF AVERAGE DAILY PRECIPITATION USING MULTIPLE LINEAR REGRESSION BY USING TOPOGRAPHIC AND WIND SPEED VARIABLES IN TROPICAL CLIMATE

Complex topography and wind characteristics play important roles in rising air masses and in daily spatial distribution of the precipitations in complex region. As a result, its spatial discontinuity and behaviour in complex areas can affect the spatial distribution of precipitation. In this work, a two-fold concept was used to consider both spatial discontinuity and topographic and wind speed in average daily spatial precipitation estimation using Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR) in tropical climates. First, wet and dry days were identified by the two methods. Then the two models based on MLR (Model 1 and Model 2) were applied on wet days to estimate the precipitation using selected predictor variables. The models were applied for month wise, season wise and year wise daily averages separately during the study period. The study reveals that, Model 1 has been found to be the best in terms of categorical statistics, R2 values, bias and special distribution patterns. However, it was found that sets of different predictor variables dominates in different months, seasons and years. Furthermore, necessities of other data for further enhancement of the results were suggested.

Validation of satellite-based rainfall in Kalahari

Water resources management in arid and semi-arid areas is hampered by insufficient rainfall data, typically obtained from sparsely distributed rain gauges. Satellite-based rainfall estimates (SREs) are alternative sources of such data in these areas. In this study, daily rainfall estimates from FEWS-RFE∼11 km, TRMM-3B42∼27 km, CMOPRH∼27 km and CMORPH∼8 km were evaluated against nine, daily rain gauge records in Central Kalahari Basin (CKB), over a five-year period, 01/01/2001-31/12/2005. The aims were to evaluate the daily rainfall detection capabilities of the four SRE algorithms, analyze the spatio-temporal variability of rainfall in the CKB and perform bias-correction of the four SREs. Evaluation methods included scatter plot analysis, descriptive statistics, categorical statistics and bias decomposition. The spatio-temporal variability of rainfall, was assessed using the SREs' mean annual rainfall, standard deviation, coefficient of variation and spatial correlation functions. Bias correction of the four SREs was conducted using a Time-Varying Space-Fixed bias-correction scheme. The results underlined the importance of validating daily SREs, as they had different rainfall detection capabilities in the CKB. The FEWS-RFE∼11 km performed best, providing better results of descriptive and categorical statistics than the other three SREs, although bias decomposition showed that all SREs underestimated rainfall. The analysis showed that the most reliable SREs performance analysis indicator were the frequency of “miss” rainfall events and the “miss-bias”, as they directly indicated SREs' sensitivity and bias of rainfall detection, respectively. The Time Varying and Space Fixed (TVSF) bias-correction scheme, improved some error measures but resulted in the reduction of the spatial correlation distance, thus increased, already high, spatial rainfall variability of all the four SREs. This study highlighted SREs as valuable source of daily rainfall data providing good spatio-temporal data coverage especially suitable for areas with limited rain gauges, such as the CKB, but also emphasized SREs' drawbacks, creating avenue for follow up research.

Nowcasting model of low wind profile based on neural network using SODAR data at Guarulhos Airport, Brazil

ABSTRACTA generalized regression neural network model was tested – as a nowcasting tool – to forecast the low wind profiles up to 45 min (i.e. at heights of 10, 100, 200, and 300 m) at the Guarulhos International Airport, São Paulo, Brazil. A data set representing over 4 years was generated from sonic detection and ranging and surface meteorological station, which registered vertical wind profiles with intervals from 10 m to approximately 500 m in height every 15 min, and surface meteorological variables were collected each minute, respectively. These data were simultaneously used to train, validate, and test the proposed model. The u and v forecasts generated at 300, 200, and 100 m were better than at 10 m, which could certainly be attributable to the surface roughness. In addition, the results also revealed that the performance of the model is time-dependent – decreasing over time – and that this may be correlated with the fact that the neural network is a statistical rather than physical model. The forecasts of wind components u and v are slightly biased (or closely matched to observations) at all heights, and forecast intervals with maximum values have median and average errors equal to 0.070 and −0.017 ms−1, respectively. The forecast model’s results were evaluated using the values of four categorical statistics: probability of detection; probability for non-events; bias; and false-alarm ratio, with respectable minimum and maximum values for u wind principal components equal to 0.841, 0.833, 0.159, 0.981 at 10 m for 45-min forecasts and 0.989, 0.987, 0.011, 0.999 at 300 m for 15-min forecasts, respectively.

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

Satellite rainfall products have different performances in different geographic regions under different physical and climatological conditions. In this study, the objective was to select the most reliable and accurate satellite rainfall products for specific, environmental conditions of Bali Island. The performances of four spatio-temporal satellite rainfall products, i.e., CMORPH25, CMORPH8, TRMM, and PERSIANN, were evaluated at the island, zonation (applying elevation and climatology as constraints), and pixel scales, using (i) descriptive statistics and (ii) categorical statistics, including bias decomposition. The results showed that all the satellite products had low accuracy because of spatial scale effect, daily resolution and the island complexity. That accuracy was relatively lower in (i) dry seasons and dry climatic zones than in wet seasons and wet climatic zones; (ii) pixels jointly covered by sea and mountainous land than in pixels covered by land or by sea only; and (iii) topographically diverse than uniform terrains. CMORPH25, CMORPH8, and TRMM underestimated and PERSIANN overestimated rainfall when comparing them to gauged rain. The CMORPH25 had relatively the best performance and the PERSIANN had the worst performance in the Bali Island. The CMORPH25 had the lowest statistical errors, the lowest miss, and the highest hit rainfall events; it also had the lowest miss rainfall bias and was relatively the most accurate in detecting, frequent in Bali, ≤ 20 mm day−1 rain events. Lastly, the CMORPH25 coarse grid better represented rainfall events from coastal to inlands areas than other satellite products, including finer grid CMORPH8.

KETERLAKSANAAN STANDAR PENGELOLAAN PENDIDIKAN DI SMA TERAKREDITASI

<p><em>This study aims: (1) to find out the significance of differences in the implementation of the Education Management Standards in Semarang Regency Accredited Senior High School, and (2) to know the implementation of components in the Education Management Standards at Senior High School (SHS) accredited A and B in Semarang Regency which is still in the Very Low category. The type of this research is descriptive comparative with quantitative approach. The sample in this study consists of 6 Accredited A and 3 Accredited B SHSs. Data or information obtained from the Principals, Vice Principals and Teachers. There are 30 respondents from 6 Accredited A and 15 people from 3 Accredited B Senior High Schools. This study used a Monitoring and Evaluation of Education Management Standards questionnaire developed by BSNP 2012. The results showed that the average in Accredited A are higher from Accredited B SHSs. While from the comparative test results obtained: there is no significant difference in the implementation of education management standards between Accredited A and Accredited B Senior High Schools in Semarang Regency. The result of the categorization statistics is the result that there is no component whose implementation in the Very Low category in both Accredited A and Accredited B Senior High School. However, there are components whose implementation is still in the Moderate category of the Component Management Information System.</em></p>

An Evaluation of WRF Rainfall Forecasts in Taiwan during Three Mei-Yu Seasons from 2008 to 2010

Abstract The performance of the Weather Research and Forecasting (WRF) Model in quantitative precipitation forecasts (QPFs) across Taiwan during three mei-yu seasons between 2008 and 2010 is evaluated using observations from about 400 rain gauges. The QPFs, spanning a range of 12–36 h and run for two nested domains at grid sizes of 15 and 5 km, are verified. Both visual and statistical-based verification methods are used to provide complementary results. More emphasis is placed on intraseasonal variation and the diurnal cycle of mei-yu rainfall, as these aspects have been less well explored previously. While the categorical statistics indicate skill levels comparable to past studies, the model performs better for frontal rainfall in May than monsoon rainfall in June. The two WRF domains are found to capture the overall rainfall amount, its general spatial pattern, the increased rain from May to June, and the basic diurnal cycle to a reasonable extent. However, both domains exhibit a persistent eastward shift in rainfall areas throughout the season, from the upwind slope to near the ridge, mainly because of excessive daytime rainfall over the mountains that starts and ends too early (more so in June), combined with insufficient rainfall upstream (in plains and slopes) since the morning. Also, the 15-km domain has total rainfall amounts closer to the observations, but the 5-km domain suffers a larger underforecast with rainfall only at the resolvable scale. Despite this, the finer mesh is more capable of predicting the peak values and local variations in rainfall and, thus, has the same skill with higher hit percentages, especially toward the high thresholds.

Evaluation of satellite rainfall climatology using CMORPH, PERSIANN‐CDR, PERSIANN, TRMM, MSWEP over Iran

ABSTRACTIn situ rainfall data observed by gauges is the most important data in water resources management. However, these data have some limitations both spatially and temporally. With the advancements in satellite rainfall products, it is now possible to evaluate whether these products can capture the climatology of known rainfall characteristics. In this study, five satellite rainfall estimates (SREs) were evaluated against gauge data based on different rainfall regimes over Iran. The evaluated SREs are Climate Prediction Center Morphing Technique, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM), PERSIANN Climate Data Record (PERSIANN‐CDR) and the most recently available Multi‐Source Weighted‐Ensemble Precipitation (MSWEP) data. The performance of these five SREs is evaluated with respect to gauge data (total: 958 stations) in eight different climatic zones at daily, monthly, and wet/dry spells during a ten‐year period (2003–2012). Performance of SREs was evaluated using metrics of comparison based on correlation coefficient (CC), root mean square error, and relative error. The study shows that MSWEP has the highest CC (0.72) followed by TRMM (0.46) and PERSIANN‐CDR (0.43) at daily time scale. The performance of SREs varies with respect to climatic regimes, for example, the best correlation was observed in the south, the shore of Persian Gulf with ‘very hot and humid’ climate with CC values of 0.72, 0.70, and 0.82 for MSWEP, TRMM and PERSIANN‐CDR, respectively. Further, the performance of SREs was evaluated using the categorical statistics to capture the rainfall pattern based on different groups (e.g. light, moderate and heavy rainfall events). Results show that MSWEP, PERSIANN‐CDR, and TRMM performed well to distinguish rain from no‐rain condition, whereas for higher rainfall rates, PERSIANN‐CDR outperforms the other SREs.

Evaluation of Error in IMERG Precipitation Estimates under Different Topographic Conditions and Temporal Scales over Mexico

This study evaluates the precipitation product of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) over the Mexican region during the period between April 2014 and October 2015 using three different time scales for cumulative precipitation (hourly, daily and seasonal). Also, the IMERG data have been analyzed as a function of elevation given the rain gauges from the automatic meteorological stations network, located within the area of study, which are used as a reference. In the present study, continuous and categorical statistics are used to evaluate IMERG. It was found that IMERG showed better performance at the daily and seasonal time scale resolutions. While hourly precipitation estimates reached a mean correlation coefficient of 0.35, the daily and seasonal precipitation estimates achieved correlations over 0.51. In addition, the IMERG precipitation product was able to reproduce the diurnal and daily cycles of the average precipitation with a trend towards overestimating rain gauges. However, extreme precipitation events were highly underestimated, as shown by relative biases of −61% and −46% for the hourly and daily precipitation analysis, respectively. It was also found that IMERG tends to improve precipitation detection and to decrease magnitude errors over the higher terrain elevations of Mexico.

Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR

In this study, satellite-based daily precipitation estimation data from precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)-climate data record (CDR) are being evaluated in Iran. This dataset (0.25°, daily), which covers over three decades of continuous observation beginning in 1983, is evaluated using rain-gauge data for the period of 1998–2007. In addition to categorical statistics and mean annual amount and number of rainy days, ten standard extreme indices were calculated to observe the behavior of daily extremes. The results show that PERSIANN-CDR exhibits reasonable performance associated with the probability of detection and false-alarm ratio, but it overestimates precipitation in the area. Although PERSIANN-CDR mostly underestimates extreme indices, it shows relatively high correlations (between 0.6316–0.7797) for intensity indices. PERSIANN-CDR data are also used to calculate the trend in annual amounts of precipitation, the number of rainy days, and precipitation extremes over Iran covering the period of 1983–2012. Our analysis shows that, although annual precipitation decreased in the western and eastern regions of Iran, the annual number of rainy days increased in the northern and northwestern areas. Statistically significant negative trends are identified in the 90th percentile daily precipitation, as well as the mean daily precipitation from wet days in the northern part of the study area. The positive trends of the maximum annual number of consecutive dry days in the eastern regions indicate that the dry periods became longer in these arid areas.

Evaluation and comparison of satellite-based rainfall products in Burkina Faso, West Africa

ABSTRACTThe performance of seven operational high-resolution satellite-based rainfall products – Africa Rainfall Estimate Climatology (ARC 2.0), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Estimation (RFE 2.0), Tropical Applications of Meteorology using SATellite (TAMSAT), African Rainfall Climatology and Time-series (TARCAT), and Tropical Rainfall Measuring Mission (TRMM) daily and monthly estimates – was investigated for Burkina Faso. These were compared to ground data for 2001–2014 on a point-to-pixel basis at daily to annual time steps. Continuous statistics was used to assess their performance in estimating and reproducing rainfall amounts, and categorical statistics to evaluate rain detection capabilities. The north–south gradient of rainfall was captured by all products, which generally detected heavy rainfall events, but showed low correlation for rainfall amounts. At daily scale they performed poorly. As the time step increased, the performance improved. All (except TARCAT) provided excellent scores for Bias and Nash–Sutcliffe Efficiency coefficients, and overestimated rainfall amounts at the annual scale. RFE performed the best, whereas TARCAT was the weakest. Choice of product depends on the specific application: ARC, RFE, and TARCAT for drought monitoring, and PERSIANN, CHIRPS, and TRMM daily for flood monitoring in Burkina Faso.

An automated nowcasting model of significant instability events in the flight terminal area of Rio de Janeiro, Brazil

Abstract. This paper presents a novel model, based on neural network techniques, to produce short-term and local-specific forecasts of significant instability for flights in the terminal area of Galeão Airport, Rio de Janeiro, Brazil. Twelve years of data were used for neural network training/validation and test. Data are originally from four sources: (1) hourly meteorological observations from surface meteorological stations at five airports distributed around the study area; (2) atmospheric profiles collected twice a day at the meteorological station at Galeão Airport; (3) rain rate data collected from a network of 29 rain gauges in the study area; and (4) lightning data regularly collected by national detection networks. An investigation was undertaken regarding the capability of a neural network to produce early warning signs – or as a nowcasting tool – for significant instability events in the study area. The automated nowcasting model was tested using results from five categorical statistics, indicated in parentheses in forecasts of the first, second, and third hours, respectively, namely proportion correct (0.99, 0.97, and 0.94), BIAS (1.10, 1.42, and 2.31), the probability of detection (0.79, 0.78, and 0.67), false-alarm ratio (0.28, 0.45, and 0.73), and threat score (0.61, 0.47, and 0.25). Possible sources of error related to the test procedure are presented and discussed. The test showed that the proposed model (or neural network) can grab the physical content inside the data set, and its performance is quite encouraging for the first and second hours to nowcast significant instability events in the study area.