Pub Date : 2021-06-28DOI: 10.15191/nwajom.2021.0904
R. Leach, Chris V. Gibson
Fire meteorologists have few tools for assessing atmospheric stability in the context of wildfires. Most tools at our disposal were developed for assessing thunderstorms and general convection, and so they ignore heat and moisture supplied by the wildfire. We propose a simple parcel-based model that can be used to assess how the atmosphere will affect a growing wildfire plume by also taking into account the heat and moisture released from the fire. From this model, we can infer trends in day to day atmospheric stability as it relates to fire plumes. We can also infer how significant the appearance of a pyrocumulus cloud on the top of a fire column is. In some cases, the appearance of a pyrocumulus indicates that the fire is near if not already blowing up, whereas in other cases environmental conditions remain too stable to have a significant effect. A qualitative application of the model is demonstrated through application to a 2017 wildfire case in Western Montana.
{"title":"Assessing the Potential for Pyroconvection and Wildfire Blow Ups","authors":"R. Leach, Chris V. Gibson","doi":"10.15191/nwajom.2021.0904","DOIUrl":"https://doi.org/10.15191/nwajom.2021.0904","url":null,"abstract":"Fire meteorologists have few tools for assessing atmospheric stability in the context of wildfires. Most tools at our disposal were developed for assessing thunderstorms and general convection, and so they ignore heat and moisture supplied by the wildfire. We propose a simple parcel-based model that can be used to assess how the atmosphere will affect a growing wildfire plume by also taking into account the heat and moisture released from the fire. From this model, we can infer trends in day to day atmospheric stability as it relates to fire plumes. We can also infer how significant the appearance of a pyrocumulus cloud on the top of a fire column is. In some cases, the appearance of a pyrocumulus indicates that the fire is near if not already blowing up, whereas in other cases environmental conditions remain too stable to have a significant effect. A qualitative application of the model is demonstrated through application to a 2017 wildfire case in Western Montana.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47871962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-11DOI: 10.15191/NWAJOM.2020.0901
J. Case, L. Wood, Jonathan L. Blaes, Kristopher White, C. Hain, C. Schultz
Several historic rainfall and flooding events associated with Atlantic Basin tropical cyclones have occurred in recent years within the conterminous United States: Hurricane Joaquin (2015) in early October over South Carolina; Hurricane Harvey (2017) in late August over southeastern Texas; Hurricane Florence (2018) in September over North Carolina; and Tropical Storm Imelda (2019) in September, again over southeastern Texas. A common attribute of these events includes a dramatic transition from dry soils to exceptional flooding in a very short time. We use an observations-driven land surface model to measure the response of modeled soil moisture to these tropical cyclone rainfall events and quantify the soil moisture anomalies relative to a daily, county-based model climatology spanning 1981 to 2013. Modeled soil moisture evolution is highlighted, including a comparison of the total column (0-2 m) soil moisture percentiles (derived from analysis values) to the 1981-2013 climatological database. The South Carolina event associated with Hurricane Joaquin resulted in a sudden transition from severe drought to significant flooding in the span of a few days, due to locally 700+ mm of rainfall. The prolonged heavy rainfall associated with Hurricane Harvey resulted in record soil moisture values well in excess of the tail of the climatological distribution. The soil moisture west of the Houston, Texas, metropolitan area was anomalously dry prior to Harvey, but quickly transitioned to near saturation in the top 1 m, while east of the Houston area antecedent soil moisture values were more moist prior to the local 1200+ mm of rainfall and catastrophic flooding in the Beaumont/Port Arthur area. Hurricane Florence led to widespread 500-700+ mm of rainfall in North Carolina, and another dramatic transition from anomalously dry conditions to record wetness. Once again, with Tropical Storm Imelda, portions of southeastern Texas experienced extreme rainfall amounts up to 1000+ mm, resulting in another sharp transition from drought conditions to extreme flooding in <3 days. An experimental forecast soil moisture percentile is presented for the Imelda event, showing the potential to increase situational awareness for upcoming flooding episodes, along with a discussion of how an ensemble-based approach could be explored to address forecast model error and uncertainty.
{"title":"Soil Moisture Responses Associated with Significant Tropical Cyclone Rainfall Events","authors":"J. Case, L. Wood, Jonathan L. Blaes, Kristopher White, C. Hain, C. Schultz","doi":"10.15191/NWAJOM.2020.0901","DOIUrl":"https://doi.org/10.15191/NWAJOM.2020.0901","url":null,"abstract":"Several historic rainfall and flooding events associated with Atlantic Basin tropical cyclones have occurred in recent years within the conterminous United States: Hurricane Joaquin (2015) in early October over South Carolina; Hurricane Harvey (2017) in late August over southeastern Texas; Hurricane Florence (2018) in September over North Carolina; and Tropical Storm Imelda (2019) in September, again over southeastern Texas. A common attribute of these events includes a dramatic transition from dry soils to exceptional flooding in a very short time. We use an observations-driven land surface model to measure the response of modeled soil moisture to these tropical cyclone rainfall events and quantify the soil moisture anomalies relative to a daily, county-based model climatology spanning 1981 to 2013. Modeled soil moisture evolution is highlighted, including a comparison of the total column (0-2 m) soil moisture percentiles (derived from analysis values) to the 1981-2013 climatological database. The South Carolina event associated with Hurricane Joaquin resulted in a sudden transition from severe drought to significant flooding in the span of a few days, due to locally 700+ mm of rainfall. The prolonged heavy rainfall associated with Hurricane Harvey resulted in record soil moisture values well in excess of the tail of the climatological distribution. The soil moisture west of the Houston, Texas, metropolitan area was anomalously dry prior to Harvey, but quickly transitioned to near saturation in the top 1 m, while east of the Houston area antecedent soil moisture values were more moist prior to the local 1200+ mm of rainfall and catastrophic flooding in the Beaumont/Port Arthur area. Hurricane Florence led to widespread 500-700+ mm of rainfall in North Carolina, and another dramatic transition from anomalously dry conditions to record wetness. Once again, with Tropical Storm Imelda, portions of southeastern Texas experienced extreme rainfall amounts up to 1000+ mm, resulting in another sharp transition from drought conditions to extreme flooding in <3 days. An experimental forecast soil moisture percentile is presented for the Imelda event, showing the potential to increase situational awareness for upcoming flooding episodes, along with a discussion of how an ensemble-based approach could be explored to address forecast model error and uncertainty.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49120284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-28DOI: 10.15191/nwajom.2020.0811
M. Stackhouse, Jeffrey D. Colton, D. D. Phillips, Kristopher J. Sanders, Michael A. Charnick, M. P. Meyers
During the early morning hours of 9 January 2017, freezing rain developed across several valley locations in western Colorado. The resultant ice accumulation led to extremely treacherous travel conditions with hundreds of vehicle accidents reported in the vicinity of Grand Junction, Colorado and near Durango, Colorado. Additionally, widespread power outages were reported in Durango and near Steamboat Springs, Colorado. First responders were overwhelmed by the volume increase of emergency calls, and secondary services were requested from nearby municipalities to help with the increased workload. The emergency operations center in Mesa County, Colorado (Grand Junction) was activated as a result of the numerous accidents and injuries across the region. An ice storm of this magnitude has not been experienced in Grand Junction’s period of record, which dates back to 1893. A detailed investigation explores the physical processes responsible for this ice storm over the complex terrain of the Intermountain West.
{"title":"A Rare Ice Storm in the Colorado Rockies","authors":"M. Stackhouse, Jeffrey D. Colton, D. D. Phillips, Kristopher J. Sanders, Michael A. Charnick, M. P. Meyers","doi":"10.15191/nwajom.2020.0811","DOIUrl":"https://doi.org/10.15191/nwajom.2020.0811","url":null,"abstract":"During the early morning hours of 9 January 2017, freezing rain developed across several valley locations in western Colorado. The resultant ice accumulation led to extremely treacherous travel conditions with hundreds of vehicle accidents reported in the vicinity of Grand Junction, Colorado and near Durango, Colorado. Additionally, widespread power outages were reported in Durango and near Steamboat Springs, Colorado. First responders were overwhelmed by the volume increase of emergency calls, and secondary services were requested from nearby municipalities to help with the increased workload. The emergency operations center in Mesa County, Colorado (Grand Junction) was activated as a result of the numerous accidents and injuries across the region. An ice storm of this magnitude has not been experienced in Grand Junction’s period of record, which dates back to 1893. A detailed investigation explores the physical processes responsible for this ice storm over the complex terrain of the Intermountain West.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43499755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-17DOI: 10.15191/nwajom.2020.0810
Jordan J. Gerth, R. Garcia, D. Hoese, S. Lindstrom, T. Schmit
The Satellite Information Familiarization Tool (SIFT) is an open-source, multi-platform graphical user interface designed to easily display spectral and temporal sequences of geostationary satellite imagery. The Advanced Baseline Imager (ABI) and Advanced Himawari Imager (AHI) on the “new generation” of geostationary satellites collect imagery with a spatial resolution four times greater than previously available. Combined with the increased number of spectral bands and more frequent imaging, the new series imagers collect approximately 60 times more data. Given the resulting large file sizes, the development of SIFT is a multiyear effort to make those satellite imagery data files accessible to the broad community of students, scientists, and operational meteorologists. To achieve the objective of releasing software that provides an intuitive user experience to complement optimum performance on consumer-grade computers, SIFT was built to leverage modern graphics processing units (GPUs) through existing open-source Python packages, and runs on the three major operating systems: Windows, Mac, and Linux. The United States National Weather Service funded the development of SIFT to help enhance the satellite meteorology acumen of their operational meteorologists. SIFT has basic image visualization capabilities and enables the fluid animation and interrogation of satellite images, creation of Red-Green-Blue (RGB) composites and algebraic combinations of multiple spectral bands, and comparison of imagery with numerical weather prediction output. Open for community development, SIFT users and features continue to grow. SIFT is freely available with short tutorials and a user guide online. The mandate for the software, its development, realized applications, and envisioned role in science and training are explained.
{"title":"SIFTing through satellite imagery with the Satellite Information Familiarization Tool","authors":"Jordan J. Gerth, R. Garcia, D. Hoese, S. Lindstrom, T. Schmit","doi":"10.15191/nwajom.2020.0810","DOIUrl":"https://doi.org/10.15191/nwajom.2020.0810","url":null,"abstract":"The Satellite Information Familiarization Tool (SIFT) is an open-source, multi-platform graphical user interface designed to easily display spectral and temporal sequences of geostationary satellite imagery. The Advanced Baseline Imager (ABI) and Advanced Himawari Imager (AHI) on the “new generation” of geostationary satellites collect imagery with a spatial resolution four times greater than previously available. Combined with the increased number of spectral bands and more frequent imaging, the new series imagers collect approximately 60 times more data. Given the resulting large file sizes, the development of SIFT is a multiyear effort to make those satellite imagery data files accessible to the broad community of students, scientists, and operational meteorologists. To achieve the objective of releasing software that provides an intuitive user experience to complement optimum performance on consumer-grade computers, SIFT was built to leverage modern graphics processing units (GPUs) through existing open-source Python packages, and runs on the three major operating systems: Windows, Mac, and Linux. The United States National Weather Service funded the development of SIFT to help enhance the satellite meteorology acumen of their operational meteorologists. SIFT has basic image visualization capabilities and enables the fluid animation and interrogation of satellite images, creation of Red-Green-Blue (RGB) composites and algebraic combinations of multiple spectral bands, and comparison of imagery with numerical weather prediction output. Open for community development, SIFT users and features continue to grow. SIFT is freely available with short tutorials and a user guide online. The mandate for the software, its development, realized applications, and envisioned role in science and training are explained.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48797174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-28DOI: 10.15191/nwajom.2020.0809
A. Houston, Janell C. Walther, L. PytlikZillig, Jake Kawamoto
The integration of unmanned aircraft systems (UAS) into the weather surveillance network must be guided by the data needs of the principal stakeholders. This work aims to assess data needs/gaps for short-term forecasts (<1-day lead time) issued by the National Weather Service (NWS) and then identify UAS characteristics required to fill these gaps. Results from focus groups and interviews of forecasters in the central United States are presented. Participant verbal responses were coded and then categorized into a set of 25 unique features. Each feature was classified according to four characteristics: 1) environmental properties that need to be measured to represent a given feature, 2) flight type (vertical profile, horizontal transect, and/or survey) 3) flight height required to measure the environmental properties, and 4) relevance of feature to the forecasting of deep convection. Findings indicate the majority of identified features require measurement of typical state variables (temperature, moisture, and wind), but more than a third require visual imagery. Almost all of the features require either survey flight operations or vertical profiles. Additionally, 96% of the features require observations collected below 1000 m. Nearly two-thirds of the features are associated with deep convection. This work represents the first step towards establishing how UAS could be used to fill data gaps that exist for short-term forecasts issued by the NWS. The results stand alone in demonstrating the potential applications of UAS from the perspective of operational forecasters and have also informed ongoing efforts to develop a nationwide survey of forecasters.
{"title":"Initial Assessment of Unmanned Aircraft System Characteristics Required to Fill Data Gaps for Short-term Forecasts: Results from Focus Groups and Interviews","authors":"A. Houston, Janell C. Walther, L. PytlikZillig, Jake Kawamoto","doi":"10.15191/nwajom.2020.0809","DOIUrl":"https://doi.org/10.15191/nwajom.2020.0809","url":null,"abstract":"The integration of unmanned aircraft systems (UAS) into the weather surveillance network must be guided by the data needs of the principal stakeholders. This work aims to assess data needs/gaps for short-term forecasts (<1-day lead time) issued by the National Weather Service (NWS) and then identify UAS characteristics required to fill these gaps. Results from focus groups and interviews of forecasters in the central United States are presented. Participant verbal responses were coded and then categorized into a set of 25 unique features. Each feature was classified according to four characteristics: 1) environmental properties that need to be measured to represent a given feature, 2) flight type (vertical profile, horizontal transect, and/or survey) 3) flight height required to measure the environmental properties, and 4) relevance of feature to the forecasting of deep convection. Findings indicate the majority of identified features require measurement of typical state variables (temperature, moisture, and wind), but more than a third require visual imagery. Almost all of the features require either survey flight operations or vertical profiles. Additionally, 96% of the features require observations collected below 1000 m. Nearly two-thirds of the features are associated with deep convection. This work represents the first step towards establishing how UAS could be used to fill data gaps that exist for short-term forecasts issued by the NWS. The results stand alone in demonstrating the potential applications of UAS from the perspective of operational forecasters and have also informed ongoing efforts to develop a nationwide survey of forecasters.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46121013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.15191/nwajom.2020.0807
Diana R. Stovern, J. A. Nelson, S. Czyzyk, M. Klein, Katie Landry-Guyton, Kristian Mattarochia, E. Nipper, J. Zeitler
A collaborative team of Science and Operations Officers from the National Weather Service (NWS) Weather Forecast Offices (WFOs), hydrologists from the Lower Mississippi River Forecast Center (LMRFC), and management from the Weather Prediction Center (WPC) worked together to develop and transition a tool into NWS operations called the Extreme Precipitation Forecast Table (EPFT). The EPFT was designed to help NWS forecasters improve their situational awareness (SA) when heavy rainfall threatens their county warning area. The EPFT compares Quantitative Precipitation Forecasts (QPF) to Average Recurrence Intervals (ARIs) from the NOAA Atlas-14 to alert forecasters to the potential for climatologically significant and extreme rainfall. A counterpart to the EPFT, called the Extreme Precipitation Assessment Table (EPAT), compares observed precipitation (i.e., Quantitative Precipitation Estimates [QPE]) to inform forecasters as to the climatological significance of impactful rain events. This paper presents cases demonstrating the usefulness of the EPFT and EPAT in helping forecasters improve their SA in real-time operational settings when heavy rain was a threat.
{"title":"The Extreme Precipitation Forecast Table: improving situational awareness when heavy rain is a threat","authors":"Diana R. Stovern, J. A. Nelson, S. Czyzyk, M. Klein, Katie Landry-Guyton, Kristian Mattarochia, E. Nipper, J. Zeitler","doi":"10.15191/nwajom.2020.0807","DOIUrl":"https://doi.org/10.15191/nwajom.2020.0807","url":null,"abstract":"A collaborative team of Science and Operations Officers from the National Weather Service (NWS) Weather Forecast Offices (WFOs), hydrologists from the Lower Mississippi River Forecast Center (LMRFC), and management from the Weather Prediction Center (WPC) worked together to develop and transition a tool into NWS operations called the Extreme Precipitation Forecast Table (EPFT). The EPFT was designed to help NWS\u0000forecasters improve their situational awareness (SA) when heavy rainfall threatens their county warning area. The EPFT compares Quantitative Precipitation Forecasts (QPF) to Average Recurrence Intervals (ARIs) from the NOAA Atlas-14 to alert forecasters to the potential for climatologically significant and extreme rainfall. A counterpart to the EPFT, called the Extreme Precipitation Assessment Table (EPAT), compares observed\u0000precipitation (i.e., Quantitative Precipitation Estimates [QPE]) to inform forecasters as to the climatological significance of impactful rain events. This paper presents cases demonstrating the usefulness of the EPFT and EPAT in helping forecasters improve their SA in real-time operational settings when heavy rain was a threat.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47878475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-20DOI: 10.15191/nwajom.2020.0803
D. Turner, J. Hamilton, W. Moninger, M. Smith, B. Strong, R. Pierce, V. Hagerty, K. Holub, S. Benjamin
Developing and improving numerical weather prediction models such as the Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) requires a well-designed, easy-to-use evaluation capability using observations. Owing to the very complex nonlinear interactions between the data assimilation system and the representation of various physics components in the model, changes to one aspect of the modeling system to address a particular shortcoming within the model may have detrimental impacts in another area. Thus, the model verification approach used in the Global Systems Division of the NOAA Earth System Research Laboratory—which actively develops the RAP and HRRR models and other forecasting systems—is designed to allow hypothesis-driven testing of different aspects of the model using observations. In this approach, model changes easily and quickly can be quantified by automatically comparing simulated geophysical variables against many different types of observations that are collected operationally by various agencies, including the National Weather Service. We have implemented this approach in the Model Analysis Tool Suite (MATS). A key aspect of MATS is the use of a database-driven system that stores partial sums of model minus observation pairs over specified geographical regions in order to reduce the dimensionality of the data and, thus, improve the response time of the system. These partial sums are created and stored in a manner that allows the data to be visualized in different ways, thereby providing new insights into the ability of that particular version of the model to replicate the observed atmospheric conditions.
{"title":"A Verification Approach Used in Developing the Rapid Refresh and Other Numerical Weather Prediction Models","authors":"D. Turner, J. Hamilton, W. Moninger, M. Smith, B. Strong, R. Pierce, V. Hagerty, K. Holub, S. Benjamin","doi":"10.15191/nwajom.2020.0803","DOIUrl":"https://doi.org/10.15191/nwajom.2020.0803","url":null,"abstract":"Developing and improving numerical weather prediction models such as the Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) requires a well-designed, easy-to-use evaluation capability using observations. Owing to the very complex nonlinear interactions between the data assimilation system and the representation of various physics components in the model, changes to one aspect of the modeling system\u0000to address a particular shortcoming within the model may have detrimental impacts in another area. Thus, the model verification approach used in the Global Systems Division of the NOAA Earth System Research Laboratory—which actively develops the RAP and HRRR models and other forecasting systems—is designed\u0000to allow hypothesis-driven testing of different aspects of the model using observations. In this approach, model changes easily and quickly can be quantified by automatically comparing simulated geophysical variables against many different types of observations that are collected operationally by various agencies, including the\u0000National Weather Service. We have implemented this approach in the Model Analysis Tool Suite (MATS). A key aspect of MATS is the use of a database-driven system that stores partial sums of model minus observation pairs over specified geographical regions in order to reduce the dimensionality of the data and, thus, improve\u0000the response time of the system. These partial sums are created and stored in a manner that allows the data to be visualized in different ways, thereby providing new insights into the ability of that particular version of the model to replicate the observed atmospheric conditions.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48585176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.15191/nwajom.2020.0808
T. Todd Lindley, A. B. Zwink, Chad M. Gravelle, Christopher C. Schmidt, Cynthia K. Palmer, Scott T. Rowe, Robyn Heffernan, N. Driscoll, Graham M. Kent
Corroboration of Geostationary Operational Environmental Satellite-17 (GOES-17) wildland fire detection capabilities occurred during the 24 October 2019 (evening of 23 October LST) ignition of the Kincade Fire in northern California. Post-analysis of remote sensing data compared to observations by the ALERTWildfire fire surveillance video system suggests that the emerging Kincade Fire hotspot was visually evident in GOES17 shortwave infrared imagery 52 s after the initial near-infrared heat source detected by the ground-based camera network. GOES-17 Advanced Baseline Imager Fire Detection Characteristic algorithms registered the fire 5 min after ignition. These observations represent the first documented comparative dataset between fire initiation and satellite detection, and thus provide context for GOES-16/17 wildland fire detections.
{"title":"Ground-Based Corroboration of GOES-17 Fire Detection Capabilities During Ignition of the Kincade Fire","authors":"T. Todd Lindley, A. B. Zwink, Chad M. Gravelle, Christopher C. Schmidt, Cynthia K. Palmer, Scott T. Rowe, Robyn Heffernan, N. Driscoll, Graham M. Kent","doi":"10.15191/nwajom.2020.0808","DOIUrl":"https://doi.org/10.15191/nwajom.2020.0808","url":null,"abstract":"Corroboration of Geostationary Operational Environmental Satellite-17 (GOES-17) wildland fire detection capabilities occurred during the 24 October 2019 (evening of 23 October LST) ignition of the Kincade Fire in northern California. Post-analysis of remote sensing data compared to observations by the ALERTWildfire fire surveillance video system suggests that the emerging Kincade Fire hotspot was visually evident in GOES17 shortwave infrared imagery 52 s after the initial near-infrared heat source detected by the ground-based camera network. GOES-17 Advanced Baseline Imager Fire Detection Characteristic algorithms registered the fire 5 min after ignition. These observations represent the first documented comparative dataset between fire initiation and satellite detection, and thus provide context for GOES-16/17 wildland fire detections.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66856221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-05DOI: 10.15191/nwajom.2019.0712
T. Lindley, Douglas A. Speheger, Matthew Day, G. Murdoch, Bradley R. Smith, Nicholas J. Nauslar, Drew C. Daily
A global increase in megafires has occurred since the mid-1990s. Defined as wildfires that burn more than 405 km2 (100 000 ac), megafires are complex phenomena with wide ranging societal impacts. In the United States, scientific literature and wildland fire policy has traditionally focused upon megafires in forests of the American West. However, megafires also pose a significant threat to life and property on the southern Great Plains. The southern Great Plains is characterized by grass-dominated prairie and is climatologically prone to dry and windy weather, which facilitates extreme rates of fire spread leading to some of the largest wildfires in North America. This study documents 16 megafires on the plains of New Mexico, Texas, Oklahoma, and Kansas between 2006 and 2018. Most of these megafires occurred during southern Great Plains wildfire outbreaks, or plains firestorms, characterized by fire-effective low-level thermal ridges. Fuel and weather conditions supporting the 2006–2018 plains megafires are quantified by antecedent precipitation anomalies, fuel moisture, Energy Release Component, relative humidity, sustained wind speed, and temperature percentiles. Three modes of plains megafire evolution are identified by the analyses as short-duration, long-duration, and hybrid. Abrupt wind shifts and carryover fire in heavy dead fuels dictate megafire potential and evolutionary type. The presented analyses define favorable fuel and weather conditions that allow forecasters to discriminate megafire environments from typical plains fire episodes. Further, predictive signals for plains megafire conceptual model types can improve anticipation of southern Great Plains megafire evolution, threats, and management strategies.
{"title":"Megafires on the Southern Great Plains","authors":"T. Lindley, Douglas A. Speheger, Matthew Day, G. Murdoch, Bradley R. Smith, Nicholas J. Nauslar, Drew C. Daily","doi":"10.15191/nwajom.2019.0712","DOIUrl":"https://doi.org/10.15191/nwajom.2019.0712","url":null,"abstract":"A global increase in megafires has occurred since the mid-1990s. Defined as wildfires that burn more than 405 km2 (100 000 ac), megafires are complex phenomena with wide ranging societal impacts. In the United States, scientific literature and wildland fire policy has traditionally focused upon megafires in forests of the American West. However, megafires also pose a significant threat to life and property on the southern Great Plains. The southern Great Plains is characterized by grass-dominated prairie and is climatologically prone to dry and windy weather, which facilitates extreme rates of fire spread leading to some of the largest wildfires in North America. This study documents 16 megafires on the plains of New Mexico, Texas, Oklahoma, and Kansas between 2006 and 2018. Most of these megafires occurred during southern Great Plains wildfire outbreaks, or plains firestorms, characterized by fire-effective low-level thermal ridges. Fuel and weather conditions supporting the 2006–2018 plains megafires are quantified by antecedent precipitation anomalies, fuel moisture, Energy Release Component, relative humidity, sustained wind speed, and temperature percentiles. Three modes of plains megafire evolution are identified by the analyses as short-duration, long-duration, and hybrid. Abrupt wind shifts and carryover fire in heavy dead fuels dictate megafire potential and evolutionary type. The presented analyses define favorable fuel and weather conditions that allow forecasters to discriminate megafire environments from typical plains fire episodes. Further, predictive signals for plains megafire conceptual model types can improve anticipation of southern Great Plains megafire evolution, threats, and management strategies.","PeriodicalId":44039,"journal":{"name":"Journal of Operational Meteorology","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42058789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-08DOI: 10.15191/nwajom.2019.0711
G. Murdoch, Christopher M. Gitro, T. Lindley, V. Mahale
To date, the use of Doppler radar (WSR-88D) in wildland fire operations has been limited, with tactical applications focused on analyzing ambient atmospheric features. This paper presents geographically diverse analysis of radar-observed wildland fire convective plumes to determine indicators of plume mode for tactical decision support. Through the visualization of buoyancy via thermal bubbles and vertical plumes, plume mode is revealed via WSR-88D interrogation of three Southern Great Plains grass/shrub fires and two timber fires in Texas and California. Analogous to thunderstorm convective modes, past research has identified two distinct plume modes of wildland fire: multicell and intense convective plume. Multicell plume mode is characterized by a series of shallow discrete cells that move away from the fire’s main buoyancy source, with successive cells rising, expanding, and replacing cells from the updraft source. This process, known as the thermal bubble concept, occurs most notably in strong vertical wind profile environments with a strong advection component. These cells or thermal bubbles are observed via WSR-88D data for three Southern Great Plains cases. Intense convective plumes are observed to be vertical with the low-level reflectivity maximum and maximum echo top juxtaposed and occurrence is confined to weak wind environments; these plume structures are identified in the two timber fire cases. An important WSR-88D signature, the back-sheared convective plume (hereafter BSCP), is identified in terms of transverse vortices and vortex rings, which may imply enhanced combustion rates due to increased turbulent mixing. Determination of plume convective mode via radar offers meteorologists the ability to detect changes in plume mode and to provide important tactical decision support information about fire behavior.
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