Oklahoma Mesonet Standard Observations

1994 ◽  
Author(s):  
◽  
Chris Fiebrich ◽  
Brad Illston
Keyword(s):  
Oklahoma Mesonet

Oklahoma Mesonet Research - Anemometer Anti-icing

1994 ◽  
Author(s):  
◽  
Chris Fiebrich ◽  
Brad Illston
Keyword(s):  
Oklahoma Mesonet

scholarly journals MesoSoil v2.0: An updated soil physical property database for the Oklahoma Mesonet

2021 ◽  
Author(s):  
Briana M. Wyatt ◽  
Tyson E. Ochsner ◽  
William G. Brown ◽  
D. Cole Diggins ◽  
Bradley G. Illston ◽  
...  
Keyword(s):  
Physical Property ◽  
Soil Physical Property ◽  
Oklahoma Mesonet

Polarimetric Radar Rain Estimation through Retrieval of Drop Size Distribution Using a Bayesian Approach

2010 ◽  
Vol 49 (5) ◽  
pp. 973-990 ◽  
Author(s):  
Qing Cao ◽  
Guifu Zhang ◽  
Edward A. Brandes ◽  
Terry J. Schuur
Keyword(s):  
Bayesian Approach ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Normal Function ◽  
In Situ Measurements ◽  
Retrieval Algorithm ◽  
Convective System ◽  
Horizontal Polarization ◽  
Oklahoma Mesonet

Abstract This study proposes a Bayesian approach to retrieve raindrop size distributions (DSDs) and to estimate rainfall rates from radar reflectivity in horizontal polarization ZH and differential reflectivity ZDR. With this approach, the authors apply a constrained-gamma model with an updated constraining relation to retrieve DSD parameters. Long-term DSD measurements made in central Oklahoma by the two-dimensional video disdrometer (2DVD) are first used to construct a prior probability density function (PDF) of DSD parameters, which are estimated using truncated gamma fits to the second, fourth, and sixth moments of the distributions. The forward models of ZH and ZDR are then developed based on a T-matrix calculation of raindrop backscattering amplitude with the assumption of drop shape. The conditional PDF of ZH and ZDR is assumed to be a bivariate normal function with appropriate standard deviations. The Bayesian algorithm has a good performance according to the evaluation with simulated ZH and ZDR. The algorithm is also tested on S-band radar data for a mesoscale convective system that passed over central Oklahoma on 13 May 2005. Retrievals of rainfall rates and 1-h rain accumulations are compared with in situ measurements from one 2DVD and six Oklahoma Mesonet rain gauges, located at distances of 28–54 km from Norman, Oklahoma. Results show that the rain estimates from the retrieval agree well with the in situ measurements, demonstrating the validity of the Bayesian retrieval algorithm.

scholarly journals Oklahoma Mesonet Soil Moisture (OKMSOIL) Value-Added Product Report

2019 ◽  
Author(s):  
L Gregory ◽  
Alice Cialella ◽  
Scott Giangrande
Keyword(s):  
Soil Moisture ◽  
Value Added ◽  
Product Report ◽  
Oklahoma Mesonet

scholarly journals A Modified Framework for Quantifying Land–Atmosphere Covariability during Hydrometeorological and Soil Wetness Extremes in Oklahoma

2019 ◽  
Vol 58 (7) ◽  
pp. 1465-1483 ◽  
Author(s):  
Ryann A. Wakefield ◽  
Jeffrey B. Basara ◽  
Jason C. Furtado ◽  
Bradley G. Illston ◽  
Craig. R. Ferguson ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
Hot Spot ◽  
Extreme Rainfall ◽  
Southern Great Plains ◽  
Low Level ◽  
Oklahoma Mesonet ◽  
Humidity Index ◽  
Grid Points ◽  
Regional Reanalysis

AbstractGlobal “hot spots” for land–atmosphere coupling have been identified through various modeling studies—both local and global in scope. One hot spot that is common to many of these analyses is the U.S. southern Great Plains (SGP). In this study, we perform a mesoscale analysis, enabled by the Oklahoma Mesonet, that bridges the spatial and temporal gaps between preceding local and global analyses of coupling. We focus primarily on east–west variations in seasonal coupling in the context of interannual variability over the period spanning 2000–15. Using North American Regional Reanalysis (NARR)-derived standardized anomalies of convective triggering potential (CTP) and the low-level humidity index (HI), we investigate changes in the covariance of soil moisture and the atmospheric low-level thermodynamic profile during seasonal hydrometeorological extremes. Daily CTP and HI z scores, dependent upon climatology at individual NARR grid points, were computed and compared to in situ soil moisture observations at the nearest mesonet station to provide nearly collocated annual composites over dry and wet soils. Extreme dry and wet year CTP and HI z-score distributions are shown to deviate significantly from climatology and therefore may constitute atmospheric precursors to extreme events. The most extreme rainfall years differ from climatology but also from one another, indicating variability in the strength of land–atmosphere coupling during these years. Overall, the covariance between soil moisture and CTP/HI is much greater during drought years, and coupling appears more consistent. For example, propagation of drought during 2011 occurred under antecedent CTP and HI conditions that were identified by this study as being conducive to positive dry feedbacks demonstrating potential utility of this framework in forecasting regional drought propagation.

scholarly journals Estimating profile soil moisture and groundwater variations using GRACE and Oklahoma Mesonet soil moisture data

2008 ◽  
Vol 44 (1) ◽  
Author(s):  
Sean Swenson ◽  
James Famiglietti ◽  
Jeffrey Basara ◽  
John Wahr
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Data ◽  
Oklahoma Mesonet

Retrieval of an Available Water-Based Soil Moisture Proxy from Thermal Infrared Remote Sensing. Part I: Methodology and Validation

2009 ◽  
Vol 10 (3) ◽  
pp. 665-683 ◽  
Author(s):  
Christopher R. Hain ◽  
John R. Mecikalski ◽  
Martha C. Anderson
Keyword(s):  
Soil Moisture ◽  
Potential Evapotranspiration ◽  
Thermal Infrared ◽  
Surface Energy Budget ◽  
Rooting Depth ◽  
Thermal Infrared Remote Sensing ◽  
Water Fraction ◽  
Available Water ◽  
Oklahoma Mesonet ◽  
Moisture Conditions

Abstract A retrieval of available water fraction ( fAW) is proposed using surface flux estimates from satellite-based thermal infrared (TIR) imagery and the Atmosphere–Land Exchange Inversion (ALEXI) model. Available water serves as a proxy for soil moisture conditions, where fAW can be converted to volumetric soil moisture through two soil texture dependents parameters—field capacity and permanent wilting point. The ability of ALEXI to provide valuable information about the partitioning of the surface energy budget, which can be largely dictated by soil moisture conditions, accommodates the retrieval of an average fAW over the surface to the rooting depth of the active vegetation. For this method, the fraction of actual to potential evapotranspiration ( fPET) is computed from an ALEXI estimate of latent heat flux and potential evapotranspiration (PET). The ALEXI-estimated fPET can be related to fAW in the soil profile. Four unique fPET to fAW relationships are proposed and validated against Oklahoma Mesonet soil moisture observations within a series of composite periods during the warm seasons of 2002–04. Using the validation results, the most representative of the four relationships is chosen and shown to produce reasonable (mean absolute errors values less than 20%) fAW estimates when compared to Oklahoma Mesonet observations. Quantitative comparisons between ALEXI and modeled fAW estimates from the Eta Data Assimilation System (EDAS) are also performed to assess the possible advantages of using ALEXI soil moisture estimates within numerical weather predication (NWP) simulations. This TIR retrieval technique is advantageous over microwave techniques because of the ability to indirectly sense fAW—and hence soil moisture conditions—extending into the root-zone layer. Retrievals are also possible over dense vegetation cover and are available on spatial resolutions on the order of the native TIR imagery. A notable disadvantage is the inability to retrieve fAW conditions through cloud cover.

scholarly journals Broadband Albedo Observations in the Southern Great Plains

2006 ◽  
Vol 45 (1) ◽  
pp. 210-235 ◽  
Author(s):  
Claude E. Duchon ◽  
Kenneth G. Hamm
Keyword(s):  
Time Series ◽  
Great Plains ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geographic Location ◽  
Bare Soil ◽  
Southern Great Plains ◽  
Oklahoma Mesonet ◽  
The One

Abstract Time series of daily broadband surface albedo for 1998 and 1999 have been analyzed from six locations in the network of 22 Atmospheric Radiation Measurement Program Solar–Infrared Radiation Stations distributed from central Kansas to central Oklahoma. Two of the stations are in Kansas, and four are in Oklahoma; together they reasonably encompass the variation in geography in the southern Great Plains. Daily precipitation totals locally measured or obtained from nearby Oklahoma Mesonet stations and time series of biweekly maximum normalized difference vegetation index obtained from NOAA’s Advanced Very High Resolution Radiometer were used to determine linkages between surface albedo and amount of precipitation and degree of green vegetation. As part of this determination, daily albedo was categorized according to sky condition, that is, clear, partly cloudy, or overcast, with appropriate boundaries for each category. The more notable results are the following: 1) 2-yr mean annual albedos varied by more than 20% among the six sites, the lowest albedo being 0.18 and the highest albedo being 0.22; 2) the numerical difference was about 4 times the maximum interannual mean difference among the six stations, indicating the importance of geographic location; 3) for sites with a large amount of bare soil, a systematic decrease in albedo in response to rainfall events and a systematic increase in albedo as the soil dried were observed; 4) at the one site with total vegetation cover, that is, no bare soil, albedo response to precipitation events was suppressed; 5) no relation was found between mean annual albedo and annual precipitation; 6) whether days were classified as clear or partly cloudy had little influence on daily albedo, but overcast days typically reduced albedo, sometimes substantially; and 7) the main contributor to low albedos on overcast days with rain was the wet surface; the contribution by the overcast sky was secondary.

Benefits and Beneficiaries of the Oklahoma Mesonet: A Multisectoral Ripple Effect Analysis

2017 ◽  
Vol 9 (3) ◽  
pp. 499-519 ◽  
Author(s):  
Jadwiga R. Ziolkowska ◽  
Christopher A. Fiebrich ◽  
J. D. Carlson ◽  
Andrea D. Melvin ◽  
Albert J. Sutherland ◽  
...  
Keyword(s):  
Public Safety ◽  
Agricultural Sector ◽  
Wildland Fire ◽  
Value Added ◽  
Economic Benefits ◽  
Decision Makers ◽  
Extreme Weather Events ◽  
Ripple Effect ◽  
Oklahoma Mesonet ◽  
K 12

Abstract Since the Oklahoma Mesonet (the state’s automated mesoscale weather station network) was established in 1994, it has served a number of diverse groups and provided public services to foster weather preparedness, education, and public safety, while also supporting decision-making in agricultural production and wildland fire management. With 121 monitoring stations across the state, the Oklahoma Mesonet has developed an array of technologies to observe a variety of atmospheric and soil variables in 5- to 30-min intervals. These consistent observations have been especially critical for predicting and preparing for extreme weather events like droughts, floods, ice storms, and severe convective storms as well as for development of value-added tools. The tools, outreach programs, and mesoscale data have been widely utilized by the general public, state decision-makers, public safety officials, K–12 community, agricultural sector, and researchers, thus generating wide societal and economic benefits to many groups. Based on practical application examples of weather information provided by the Oklahoma Mesonet, this paper analyzes both benefits generated by Oklahoma Mesonet information to the public and decision-makers and ripple effects (spreading amplified outcomes/implications) of those benefits in the short and long term. The paper further details ongoing and anticipated Oklahoma Mesonet innovations as a response to changing needs for weather-related information over time, especially as a result of technological developments and weather variability.

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