Cloud — Aerosol interaction during lightning activity over land and ocean: Precipitation pattern assessment

The present study attempts to identify the land - ocean contrast in cloud - aerosol relation during lightning and non-lightning days and its effect on subsequent precipitation pattern. The thermal hypothesis in view of Convective Available Potential Energy (CAPE) behind the land - ocean contrast is observed to be insignificant in the present study region. The result shows that the lightning activities are significantly and positively correlated with aerosols over both land and ocean in case of low aerosol loading whereas for high aerosol loading the correlation is significant but, only over land. The study attempts to comprehend the mechanism through which the aerosol and lightning interact using the concept of aerosol indirect effect that includes the study of cloud effective radius, cloud fraction and precipitation rate. The result shows that the increase in lightning activity over ocean might have been caused due to the first aerosol indirect effect, while over land the aerosol indirect effect might have been suppressed due to lightning. Thus, depending on the region and relation between cloud parameters it is observed that the precipitation rate decreases (increases) over ocean during lightning (non-lightning) days. On the other hand during non-lightning days, the precipitation rate decreases over land.     

A technical comment: Analysis of ‘examination of “global atmospheric temperature monitoring with satellite microwave measurements”’

The potential for residual hydrometeor contamination effects in the global temperature time series produced by Spencer and Christy from MSU channel 2 (MSU2) data has been addressed by Prabhakara et al. (1995, 1996). They use tropical oceanic MSU channel l (MSU1) data to estimate the hydrometeor effects on MSU2. We present several lines of evidence to show that their technique greatly overestimates the hydrometeor effects on MSU2. This overestimation is due to the faulty assumption that the hydrometeors that cause MSU1 warming are the same as (or always exist with) the hydrometeors that cause cooling in MSU2. Instead, the hydrometeors responsible for MSU1 warming are liquid phase, while those responsible for MSU2 cooling are large ice particles. Because liquid phase clouds are much more widespread than the large-ice portions of deep convective systems, their method greatly overestimates the areal coverage of contaminated tropical MSU2 data. In addition, we show that the convective screening procedure of Spencer and Christy removes the negative correlation between MSU1 and MSU2 their conclusions rest upon. Radiosonde validation of monthly tropical MSU2 anomalies over the tropical West Pacific also support these conclusions.     

Simulation of the annual and diurnal cycles of rainfall over South Africa by a regional climate model

The capability of a current state-of-the-art regional climate model for simulating the diurnal and annual cycles of rainfall over a complex subtropical region is documented here. Hourly rainfall is simulated over Southern Africa for 1998–2006 by the non-hydrostatic model weather research and forecasting (WRF), and compared to a network of 103 stations covering South Africa. We used five simulations, four of which consist of different parameterizations for atmospheric convection at a 0.5 × 0.5° resolution, performed to test the physic-dependency of the results. The fifth experiment uses explicit convection over tropical South Africa at a 1/30° resolution. WRF simulates realistic mean rainfall fields, albeit wet biases over tropical Africa. The model mean biases are strongly modulated by the convective scheme used for the simulations. The annual cycle of rainfall is well simulated over South Africa, mostly influenced by tropical summer rainfall except in the Western Cape region experiencing winter rainfall. The diurnal cycle shows a timing bias, with atmospheric convection occurring too early in the afternoon, and causing too abundant rainfall. This result, particularly true in summer over the northeastern part of the country, is weakly physic-dependent. Cloud-resolving simulations do not clearly reduce the diurnal cycle biases. In the end, the rainfall overestimations appear to be mostly imputable to the afternoon hours of the austral summer rainy season, i.e., the periods during which convective activity is intense over the region.     

Optimization of Nowcast Software WDSS-II for operational application over the Indian region

Nowcasting in the India Meteorological Department (IMD) is being provided for T + 0 to T + 2 h, using the Warning Decision Support System (WDSS-II) software. Prior to operational nowcasting over the Indian region, the parameters of the nowcast algorithm tool of the software were optimized, and accuracy was evaluated for various weather systems over Delhi. This optimization is demonstrated in this study with reference to three weather systems over Delhi, with each case representing one of three typical types of cloud systems over the region. These are—(a) convective lines associated with winter and early pre-monsoon weather systems, (b) deep convective cells that form in the pre-monsoon (April–June) and post-monsoon season (October–November) and (c) wide convective echoes that form during the monsoon season. The efficacy of the algorithm was assessed on a frame-by-frame basis as well as holistically for entire convective episodes. The important findings of the frame-by-frame study are (1) the inability of the inbuilt growth-decay algorithm to capture the evolution of storm cells, (2) setting of the threshold of detection of storms and tracking storms and (3) number of scales through which storms should be tracked. The holistic capabilities of the nowcast algorithm were tested for entire convective episodes using Model Evaluation Tools software. The results indicate that the advection algorithm tends to move the convective areas faster than observed at all time scales. Hence the multi-scale segmentation approach (over the two-scale approach) increases the smoothening of the output, at the cost of decreased nowcast skill. The inter-event comparison indicates that the low-intensity convective line zones, which are characteristic of winter and early pre-monsoon weather systems, have the most rapid temporal change in the overall area under convection. This leads to larger area errors during nowcasting of these systems. On the other hand, pre-monsoon systems comprised mostly isolated cells that reach great heights and move very fast, but do not have much horizontal area growth. The error in the nowcasting of these systems is mostly in respect of location error, as well as error in forecast of the intensity of the cells. The overall error in nowcasting is least for the monsoon systems over the Delhi region.     

Ten Years of Science Based on the Canadian Precipitation Analysis: A CaPA System Overview and Literature Review†

Near real-time quantitative precipitation estimates are required for many applications including weather forecasting, flood forecasting, crop management, forest fire prevention, hydropower production, and dam safety. Since April 2011, such a product has been available from Environment and Climate Change Canada for a domain covering all North America. This product, known as the Regional Deterministic Precipitation Analysis, is generated using the Canadian Precipitation Analysis (CaPA) system. Although it was designed for near real-time use, an archive of pre-operational and operational products going back to 2002 is now available and has been used in numerous studies. This paper presents a review of the various scientific publications that have reported either using or evaluating CaPA products. We find that the product is used with success both for scientific studies and operational applications and compares well with other precipitation datasets. We summarize the strengths and weaknesses of the system as reported in the literature. We also provide users with information on how the system works, how it has changed over time, and how the archived and near real-time analyses can be accessed and used. We finally briefly report on recent and upcoming improvements to the product based, in part, on the results of this literature review.     

Utilizing the Potential of World View −2 for Discriminating Urban and Vegetation Features Using Object Based Classification Techniques

With the availability of very high resolution multispectral imagery, it is possible to identify small features in urban environment. Because of the multiscale feature and diverse composition of land cover types found within the urban environment, the production of accurate urban land cover maps from high resolution satellite imagery is a difficult task. This paper demonstrates the potential of 8 bands capability of World View 2 satellite for better automated feature extraction and discrimination studies. Multiresolution segmentation and object based classification techniques were then applied for discrimination of urban and vegetation features in a part of Dehradun, Uttarakhand, India. The study demonstrates that scale, colour, shape, compactness and smoothness have a significant influence on the quality of image objects achieved, which in turn governs the classified result. The object oriented analysis is a valid approach for analyzing high spatial and spectral resolution images. World View 2 imagery with its rich spatial and spectral information content has very high potential for discrimination of the less varied varieties of vegetation.     

Evaluation of fuzzy-based classifiers for cotton crop identification

In this study, an evaluation of fuzzy-based classifiers for specific crop identification using multi-spectral temporal data spanning over one growing season has been carried out. The temporal data sets have been georeferenced with 0.3 pixel rms error. Temporal information of cotton crop has been incorporated through the following five indices: simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI) and triangular vegetation index (TVI), to study the effect of indices on classified output. For this purpose, a comparative study between two fuzzy-based soft classification approaches, possibilistic c-means (PCM) and noise classifier (NC), was undertaken. In this study, advanced wide field sensor (AWiFS) data for soft classification and linear imaging self scanner sensor (LISS III) data for soft testing purpose from Resourcesat-1 (IRS-P6) satellite were used. It has been observed that NC fuzzy classifier using TNDVI temporal index – dataset 2, which comprises four temporal images performs better than PCM classifier giving highest fuzzy overall accuracy of 96.03%.     

Characterisation of ecological parameters in tropical forest community—A remote sensing approach

Satellite Remote Sensing data has been used for vegetation mapping, initial stratification, distribution of sample plots and for calculating the area under different vegetation types. Primary and secondary analyses of vegetation has been done using phytosociological ground data collected from sample piots to assess the ecological importance of different species. Interrelationships among different communities have been evaluated through various available indices. The spatial distribution and vegetation analysis indicate that commercial extraction of natural forests of Andaman has set in retrogression. The evergreen forests subjected to shorter rotation of commercial exploitation are being invaded with seral deciduous species. The study highlights the status of forests (spatial and community) and stresses the need to conserve germplasm present in the natural evergreen forests.     

GIS professional development for teachers: lessons learned from high-needs schools

University faculty partners from the Departments of Geography and Instruction and Teacher Education at a large, public university collaborated with K-12 teachers and the leadership of a rural school district in order to investigate the crosscutting content of science, mathematics, and geography through the integration of web-based GIS technologies. The project explored the critical connections among technology, pedagogy, and content with a particular emphasis on developing technology-enhanced, inquiry-based lessons in which the teachers and their students used GIS technologies to analyze, visualize, and present data in real-world contexts. The findings highlight the importance of well-structured professional development that builds community, integrates diverse content and pedagogical expertise, provides feedback and coaching, and is of sufficient duration to effect change.     

Electricity consumption patterns within cities: application of a data-driven settlement characterization method

ABSTRACT

Urban areas presently consume around 75% of global primary energy supply, which is expected to significantly increase in the future due to urban growth. Having sustainable, universal energy access is a pressing challenge for most parts of the globe. Understanding urban energy consumption patterns may help to address the challenges to urban sustainability and energy security. However, urban energy analyses are severely limited by the lack of urban energy data. Such datasets are virtually non-existent for the developing countries. As per current projections, most of the new urban growth is bound to occur in these data-starved regions. Hence, there is an urgent need of research methods for monitoring and quantifying urban energy utilization patterns. Here, we apply a data-driven approach to characterize urban settlements based on their formality, which is then used to assess intra-urban urban energy consumption in Johannesburg, South Africa; Sana’a, Yemen; and Ndola, Zambia. Electricity is the fastest growing energy fuel. By analyzing the relationship between the settlement types and the corresponding nighttime light emission, a proxy of electricity consumption, we assess the differential electricity consumption patterns. Our study presents a simple and scalable solution to fill the present data void to understand intra-city electricity consumption patterns.