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Madhavi Jain, A. P. Dimri, and D. Niyogi

Abstract

Recent decades have witnessed rapid urbanization and urban population growth resulting in urban sprawl of cities. This paper analyzes the spatiotemporal dynamics of the urbanization process (using remote sensing and spatial metrics) that has occurred in Delhi, the capital city of India, which is divided into nine districts. The urban patterns and processes within the nine administrative districts of the city based on raw satellite data have been taken into consideration. Area, population, patch, edge, and shape metrics along with Pearson’s chi statistics and Shannon’s entropy have been calculated. Three types of urban patterns exist in the city: 1) highly sprawled districts, namely, West, North, North East, and East; 2) medium sprawled districts, namely, North West, South, and South West; and 3) least sprawled districts—Central and New Delhi. Relative entropy, which scales Shannon’s entropy values from 0 to 1, is calculated for the districts and time spans. Its values are 0.80, 0.92, and 0.50 from 1977 to 1993, 1993 to 2006, and 2006 to 2014, respectively, indicating a high degree of urban sprawl. Parametric and nonparametric correlation tests suggest the existence of associations between built-up density and population density, area-weighted mean patch fractal dimension (AWMPFD) and area-weighted mean shape index (AWMSI), compactness index and edge density, normalized compactness index and number of patches, and AWMPFD and built-up density.

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Yue Zheng, Kiran Alapaty, Jerold A. Herwehe, Anthony D. Del Genio, and Dev Niyogi

Abstract

Efforts to improve the prediction accuracy of high-resolution (1–10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at these spatial scales due to uncertainties in initial meteorological conditions and/or grid-scale cloud microphysics schemes. In particular, it is still unclear to what extent a subgrid-scale convection scheme could be modified to bring in scale awareness for improving high-resolution short-term precipitation forecasts in the WRF Model. To address these issues, the authors introduced scale-aware parameterized cloud dynamics for high-resolution forecasts by making several changes to the Kain–Fritsch (KF) convective parameterization scheme in the WRF Model. These changes include subgrid-scale cloud–radiation interactions, a dynamic adjustment time scale, impacts of cloud updraft mass fluxes on grid-scale vertical velocity, and lifting condensation level–based entrainment methodology that includes scale dependency.

A series of 48-h retrospective forecasts using a combination of three treatments of convection (KF, updated KF, and the use of no cumulus parameterization), two cloud microphysics schemes, and two types of initial condition datasets were performed over the U.S. southern Great Plains on 9- and 3-km grid spacings during the summers of 2002 and 2010. Results indicate that 1) the source of initial conditions plays a key role in high-resolution precipitation forecasting, and 2) the authors’ updated KF scheme greatly alleviates the excessive precipitation at 9-km grid spacing and improves results at 3-km grid spacing as well. Overall, the study found that the updated KF scheme incorporated into a high-resolution model does provide better forecasts for precipitation location and intensity.

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U. C. Mohanty, Krishna K. Osuri, Vijay Tallapragada, Frank D. Marks, Sujata Pattanayak, M. Mohapatra, L. S. Rathore, S. G. Gopalakrishnan, and Dev Niyogi

Abstract

The very severe cyclonic storm (VSCS) “Phailin (2013)” was the strongest cyclone that hit the eastern coast of the India Odisha state since the supercyclone of 1999. But the same story of casualties was not repeated as that of 1999 where approximately 10 000 fatalities were reported. In the case of Phailin, a record 1 million people were evacuated across 18 000 villages in both the Odisha and Andhra Pradesh states to coastal shelters following the improved operational forecast guidance that benefited from highly skillful and accurate numerical model guidance for the movement, intensity, rainfall, and storm surge. Thus, the property damage and death toll were minimized through the proactive involvement of three-tier disaster management agencies at central, state, and district levels.

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Joseph G. Alfieri, Dev Niyogi, Peter D. Blanken, Fei Chen, Margaret A. LeMone, Kenneth E. Mitchell, Michael B. Ek, and Anil Kumar

Abstract

Vegetated surfaces, such as grasslands and croplands, constitute a significant portion of the earth’s surface and play an important role in land–atmosphere exchange processes. This study focuses on one important parameter used in describing the exchange of moisture from vegetated surfaces: the minimum canopy resistance (r cmin). This parameter is used in the Jarvis canopy resistance scheme that is incorporated into the Noah and many other land surface models. By using an inverted form of the Jarvis scheme, r cmin is determined from observational data collected during the 2002 International H2O Project (IHOP_2002). The results indicate that r cmin is highly variable both site to site and over diurnal and longer time scales. The mean value at the grassland sites in this study is 96 s m−1 while the mean value for the cropland (winter wheat) sites is one-fourth that value at 24 s m−1. The mean r cmin for all the sites is 72 s m−1 with a standard deviation of 39 s m−1. This variability is due to both the empirical nature of the Jarvis scheme and a combination of changing environmental conditions, such as plant physiology and plant species composition, that are not explicitly considered by the scheme. This variability in r cmin has important implications for land surface modeling where r cmin is often parameterized as a constant. For example, the Noah land surface model parameterizes r cmin for the grasslands and croplands types in this study as 40 s m−1. Tests with the coupled Weather Research and Forecasting (WRF)–Noah model indicate that the using the modified values of r cmin from this study improves the estimates of latent heat flux; the difference between the observed and modeled moisture flux decreased by 50% or more. While land surface models that estimate transpiration using Jarvis-type relationships may be improved by revising the r cmin values for grasslands and croplands, updating the r cmin will not fully account for the variability in r cmin observed in this study. As such, it may be necessary to replace the Jarvis scheme currently used in many land surface and numerical weather prediction models with a physiologically based estimate of the canopy resistance.

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Fei Chen, Kevin W. Manning, Margaret A. LeMone, Stanley B. Trier, Joseph G. Alfieri, Rita Roberts, Mukul Tewari, Dev Niyogi, Thomas W. Horst, Steven P. Oncley, Jeffrey B. Basara, and Peter D. Blanken

Abstract

This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International H2O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)–land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF–LSM without conversion and interpolation. If HRLDAS is initialized with soil conditions previously spun up from other models, it requires roughly 8–10 months for HRLDAS to reach quasi equilibrium and is highly dependent on soil texture. However, the HRLDAS surface heat fluxes can reach quasi-equilibrium state within 3 months for most soil texture categories. Atmospheric forcing conditions used to drive HRLDAS were evaluated against Oklahoma Mesonet data, and the response of HRLDAS to typical errors in each atmospheric forcing variable was examined. HRLDAS-simulated finescale (4 km) soil moisture, temperature, and surface heat fluxes agreed well with the Oklahoma Mesonet and IHOP_2002 field data. One case study shows high correlation between HRLDAS evaporation and the low-level water vapor field derived from radar analysis.

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N. Hosannah, J. González, R. Rodriguez-Solis, H. Parsiani, F. Moshary, L. Aponte, R. Armstrong, E. Harmsen, P. Ramamurthy, M. Angeles, L. León, N. Ramírez, D. Niyogi, and B. Bornstein

Abstract

Modulated by global-, continental-, regional-, and local-scale processes, convective precipitation in coastal tropical regions is paramount in maintaining the ecological balance and socioeconomic health within them. The western coast of the Caribbean island of Puerto Rico is ideal for observing local convective dynamics as interactions between complex processes involving orography, surface heating, land cover, and sea-breeze–trade wind convergence influence different rainfall climatologies across the island. A multiseason observational effort entitled the Convection, Aerosol, and Synoptic-Effects in the Tropics (CAST) experiment was undertaken using Puerto Rico as a test case, to improve the understanding of island-scale processes and their effects on precipitation. Puerto Rico has a wide network of observational instruments, including ground weather stations, soil moisture sensors, a Next Generation Weather Radar (NEXRAD), twice-daily radiosonde launches, and Aerosol Robotic Network (AERONET) sunphotometers. To achieve the goals of CAST, researchers from multiple institutions supplemented existing observational networks with additional radiosonde launches, three high-resolution radars, continuous ceilometer monitoring, and air sampling in western Puerto Rico to monitor convective precipitation events. Observations during three CAST measurement phases (22 June–10 July 2015, 6–22 February 2016, and 24 April–7 May 2016) captured the most extreme drought in recent history (summer 2015), in addition to anomalously wet early rainfall and dry-season (2016) phases. This short article presents an overview of CAST along with selected campaign data.

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Eugene S. Takle, Christopher J. Anderson, Jeffrey Andresen, James Angel, Roger W. Elmore, Benjamin M. Gramig, Patrick Guinan, Steven Hilberg, Doug Kluck, Raymond Massey, Dev Niyogi, Jeanne M. Schneider, Martha D. Shulski, Dennis Todey, and Melissa Widhalm

Abstract

Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.

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J. Ching, G. Mills, B. Bechtel, L. See, J. Feddema, X. Wang, C. Ren, O. Brousse, A. Martilli, M. Neophytou, P. Mouzourides, I. Stewart, A. Hanna, E. Ng, M. Foley, P. Alexander, D. Aliaga, D. Niyogi, A. Shreevastava, P. Bhalachandran, V. Masson, J. Hidalgo, J. Fung, M. Andrade, A. Baklanov, W. Dai, G. Milcinski, M. Demuzere, N. Brunsell, M. Pesaresi, S. Miao, Q. Mu, F. Chen, and N. Theeuwes

Abstract

The World Urban Database and Access Portal Tools (WUDAPT) is an international community-based initiative to acquire and disseminate climate relevant data on the physical geographies of cities for modeling and analysis purposes. The current lacuna of globally consistent information on cities is a major impediment to urban climate science toward informing and developing climate mitigation and adaptation strategies at urban scales. WUDAPT consists of a database and a portal system; its database is structured into a hierarchy representing different levels of detail, and the data are acquired using innovative protocols that utilize crowdsourcing approaches, Geowiki tools, freely accessible data, and building typology archetypes. The base level of information (L0) consists of local climate zone (LCZ) maps of cities; each LCZ category is associated with a range of values for model-relevant surface descriptors (roughness, impervious surface cover, roof area, building heights, etc.). Levels 1 (L1) and 2 (L2) will provide specific intra-urban values for other relevant descriptors at greater precision, such as data morphological forms, material composition data, and energy usage. This article describes the status of the WUDAPT project and demonstrates its potential value using observations and models. As a community-based project, other researchers are encouraged to participate to help create a global urban database of value to urban climate scientists.

Open access
X. Liang, S. Miao, J. Li, R. Bornstein, X. Zhang, Y. Gao, F. Chen, X. Cao, Z. Cheng, C. Clements, W. Dabberdt, A. Ding, D. Ding, J. J. Dou, J. X. Dou, Y. Dou, C. S. B. Grimmond, J. E. González-Cruz, J. He, M. Huang, X. Huang, S. Ju, Q. Li, D. Niyogi, J. Quan, J. Sun, J. Z. Sun, M. Yu, J. Zhang, Y. Zhang, X. Zhao, Z. Zheng, and M. Zhou

Abstract

Urbanization modifies atmospheric energy and moisture balances, forming distinct features [e.g., urban heat islands (UHIs) and enhanced or decreased precipitation]. These produce significant challenges to science and society, including rapid and intense flooding, heat waves strengthened by UHIs, and air pollutant haze. The Study of Urban Impacts on Rainfall and Fog/Haze (SURF) has brought together international expertise on observations and modeling, meteorology and atmospheric chemistry, and research and operational forecasting. The SURF overall science objective is a better understanding of urban, terrain, convection, and aerosol interactions for improved forecast accuracy. Specific objectives include a) promoting cooperative international research to improve understanding of urban summer convective precipitation and winter particulate episodes via extensive field studies, b) improving high-resolution urban weather and air quality forecast models, and c) enhancing urban weather forecasts for societal applications (e.g., health, energy, hydrologic, climate change, air quality, planning, and emergency response management). Preliminary SURF observational and modeling results are shown (i.e., turbulent PBL structure, bifurcating thunderstorms, haze events, urban canopy model development, and model forecast evaluation).

Open access