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Roger Pielke Sr.
,
John Nielsen-Gammon
,
Christopher Davey
,
Jim Angel
,
Odie Bliss
,
Nolan Doesken
,
Ming Cai
,
Souleymane Fall
,
Dev Niyogi
,
Kevin Gallo
,
Robert Hale
,
Kenneth G. Hubbard
,
Xiaomao Lin
,
Hong Li
, and
Sethu Raman

The objective of this research is to determine whether poorly sited long-term surface temperature monitoring sites have been adjusted in order to provide spatially representative independent data for use in regional and global surface temperature analyses. We present detailed analyses that demonstrate the lack of independence of the poorly sited data when they are adjusted using the homogenization procedures employed in past studies, as well as discuss the uncertainties associated with undocumented station moves. We use simulation and mathematics to determine the effect of trend on station adjustments and the associated effect of trend in the reference series on the trend of the adjusted station. We also compare data before and after adjustment to the reanalysis data, and we discuss the effect of land use changes on the uncertainty of measurement.

A major conclusion of our analysis is that there are large uncertainties associated with the surface temperature trends from the poorly sited stations. Moreover, rather than providing additional independent information, the use of the data from poorly sited stations provides a false sense of confidence in the robustness of the surface temperature trend assessments.

Full access
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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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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Cenlin He
,
Fei Chen
,
Michael Barlage
,
Zong-Liang Yang
,
Jerry W. Wegiel
,
Guo-Yue Niu
,
David Gochis
,
David M. Mocko
,
Ronnie Abolafia-Rosenzweig
,
Zhe Zhang
,
Tzu-Shun Lin
,
Prasanth Valayamkunnath
,
Michael Ek
, and
Dev Niyogi
Open access
Rezaul Mahmood
,
Roger A. Pielke Sr.
,
Kenneth G. Hubbard
,
Dev Niyogi
,
Gordon Bonan
,
Peter Lawrence
,
Richard McNider
,
Clive McAlpine
,
Andres Etter
,
Samuel Gameda
,
Budong Qian
,
Andrew Carleton
,
Adriana Beltran-Przekurat
,
Thomas Chase
,
Arturo I. Quintanar
,
Jimmy O. Adegoke
,
Sajith Vezhapparambu
,
Glen Conner
,
Salvi Asefi
,
Elif Sertel
,
David R. Legates
,
Yuling Wu
,
Robert Hale
,
Oliver W. Frauenfeld
,
Anthony Watts
,
Marshall Shepherd
,
Chandana Mitra
,
Valentine G. Anantharaj
,
Souleymane Fall
,
Robert Lund
,
Anna Treviño
,
Peter Blanken
,
Jinyang Du
,
Hsin-I Chang
,
Ronnie Leeper
,
Udaysankar S. Nair
,
Scott Dobler
,
Ravinesh Deo
, and
Jozef Syktus
Full access
Akshara Kaginalkar
,
Sachin D. Ghude
,
U. C. Mohanty
,
Pradeep Mujumdar
,
Sudheer Bhakare
,
Hemant Darbari
,
Arun K. Dwivedi
,
Pallavi Gavali
,
Srujan Gavhale
,
Sahidul Islam
,
Gouri Kadam
,
Sumita Kedia
,
Manoj Khare
,
Neelesh Kharkar
,
Santosh H. Kulkarni
,
Sri Sai Meher
,
A. K. Nath
,
Mohamed Niyaz
,
Sagar Pokale
,
Vineeth Krishnan Valappil
,
Sreyashi Debnath
,
Chinmay Jena
,
Raghu Nadimpalli
,
Madhusmita Swain
,
Saimy Davis
,
Shubha Avinash
,
C. Kishtawal
,
Prashant Gargava
,
S. D. Attri
, and
Dev Niyogi

Abstract

Global urban population is projected to double by 2050. This rapid urbanization is the driver of economic growth but has environmental challenges. To that end, there is an urgent need to understand, simulate, and disseminate information about extreme events, routine city operations, and long-term planning decisions. This paper describes an effort underway in India involving an interdisciplinary community of meteorology, hydrology, air quality, and computer science from national and international institutes. The urban collaboratory is a system of systems for simulating weather, hydrology, air quality, health, energy, transport, and economy and its interactions. Study and prediction of urban events involve multiscale observations and cross-sector models, heterogeneous data management, and enormous computing power. The consortia program (NSM_Urban) is part of “weather ready cities,” under the aegis of India’s National Supercomputing Mission. The ecosystem “Urban Environment Science to Society” (UES2S) builds on the integrated cyberinfrastructure with a science gateway for community research and end-user service with modeling and interoperable data. The collaboratory has urban computing, stakeholder participation, and a coordinated means to scaffold projects and ideas into operational tools. It discusses the design and the utilization of high-performance computing (HPC) as a science cloud platform for bridging urban environment and data science, participatory stakeholder applications, and decision-making. The system currently integrates models for high-impact urban weather, flooding, air quality, and simulating street- and building-scale wind flow and dispersion. The program with the work underway is ripe for interfacing with regional and international partners, and this paper provides an avenue toward that end.

Full access