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• 1. [Article] Application of Tree-Structured Regression for Regional Precipitation Prediction Using General Circulation Model Output

  This study presents a tree-structured regression (TSR) method to relate daily precipitation with a variety of free-atmosphere variables. Historical data were used to identify distinct weather patterns ...

  Citation × Citation Citation Abstract Copy Title: Application of Tree-Structured Regression for Regional Precipitation Prediction Using General Circulation Model Output Author: Li, Xiangshang, Sailor, David J. Year: 2000 This study presents a tree-structured regression (TSR) method to relate daily precipitation with a variety of free-atmosphere variables. Historical data were used to identify distinct weather patterns associated with differing types of precipitation events. Models were developed using 67% of the data for training and the remaining data for model validation. Seasonal models were built for each of 2 US sites: San Francisco, California, and San Antonio, Texas. The average correlation between observed and simulated daily precipitation data series is 0.75 for the training set and 0.68 for the validation set. Relative humidity was found to be the dominant variable in these TSR models. Output from an NCAR CSM (climate system model) transient simulation of climate change were then used to drive the TSR models in the prediction of precipitation characteristics under climate change. A preliminary screening of the GCM output variables for current climate, however, revealed significant problems for the San Antonio site. Specifically, the CSM missed the annual trends in humidity for the grid cell containing this site. CSM output for the San Francisco site was found to be much more reliable. Therefore, we present future precipitation estimates only for the San Francisco site. Title: Application of Tree-Structured Regression for Regional Precipitation Prediction Using General Circulation Model Output Author: Li, Xiangshang, Sailor, David J. Year: 2000 This study presents a tree-structured regression (TSR) method to relate daily precipitation with a variety of free-atmosphere variables. Historical data were used to identify distinct weather patterns associated with differing types of precipitation events. Models were developed using 67% of the data for training and the remaining data for model validation. Seasonal models were built for each of 2 US sites: San Francisco, California, and San Antonio, Texas. The average correlation between observed and simulated daily precipitation data series is 0.75 for the training set and 0.68 for the validation set. Relative humidity was found to be the dominant variable in these TSR models. Output from an NCAR CSM (climate system model) transient simulation of climate change were then used to drive the TSR models in the prediction of precipitation characteristics under climate change. A preliminary screening of the GCM output variables for current climate, however, revealed significant problems for the San Antonio site. Specifically, the CSM missed the annual trends in humidity for the grid cell containing this site. CSM output for the San Francisco site was found to be much more reliable. Therefore, we present future precipitation estimates only for the San Francisco site. Close

• 2. [Article] Characterizing Large-Scale Meteorological Patterns and Associated Temperature and Precipitation Extremes over the Northwestern United States Using Self-Organizing Maps

  The self-organizing maps (SOMs) approach is demonstrated as a way to identify a range of archetypal large-scale meteorological patterns (LSMPs) over the northwestern United States and connect these patterns ...

  Citation × Citation Citation Abstract Copy Title: Characterizing Large-Scale Meteorological Patterns and Associated Temperature and Precipitation Extremes over the Northwestern United States Using Self-Organizing Maps Author: Loikith, Paul C., Lintner, Benjamin R., Sweeney, Alex Year: 2017 The self-organizing maps (SOMs) approach is demonstrated as a way to identify a range of archetypal large-scale meteorological patterns (LSMPs) over the northwestern United States and connect these patterns with local-scale temperature and precipitation extremes. SOMs are used to construct a set of 12 characteristic LSMPs (nodes) based on daily reanalysis circulation fields spanning the range of observed synoptic-scale variability for the summer and winter seasons for the period 1979–2013. Composites of surface variables are constructed for subsets of days assigned to each node to explore relationships between temperature, precipitation, and the node patterns. The SOMs approach also captures interannual variability in daily weather regime frequency related to El Niño–Southern Oscillation. Temperature and precipitation extremes in high-resolution gridded observations and in situ station data show robust relationships with particular nodes in many cases, supporting the approach as a way to identify LSMPs associated with local extremes. Assigning days from the extreme warm summer of 2015 and wet winter of 2016 to nodes illustrates how SOMs may be used to assess future changes in extremes. These results point to the applicability of SOMs to climate model evaluation and assessment of future projections of local-scale extremes without requiring simulations to reliably resolve extremes at high spatial scales. Title: Characterizing Large-Scale Meteorological Patterns and Associated Temperature and Precipitation Extremes over the Northwestern United States Using Self-Organizing Maps Author: Loikith, Paul C., Lintner, Benjamin R., Sweeney, Alex Year: 2017 The self-organizing maps (SOMs) approach is demonstrated as a way to identify a range of archetypal large-scale meteorological patterns (LSMPs) over the northwestern United States and connect these patterns with local-scale temperature and precipitation extremes. SOMs are used to construct a set of 12 characteristic LSMPs (nodes) based on daily reanalysis circulation fields spanning the range of observed synoptic-scale variability for the summer and winter seasons for the period 1979–2013. Composites of surface variables are constructed for subsets of days assigned to each node to explore relationships between temperature, precipitation, and the node patterns. The SOMs approach also captures interannual variability in daily weather regime frequency related to El Niño–Southern Oscillation. Temperature and precipitation extremes in high-resolution gridded observations and in situ station data show robust relationships with particular nodes in many cases, supporting the approach as a way to identify LSMPs associated with local extremes. Assigning days from the extreme warm summer of 2015 and wet winter of 2016 to nodes illustrates how SOMs may be used to assess future changes in extremes. These results point to the applicability of SOMs to climate model evaluation and assessment of future projections of local-scale extremes without requiring simulations to reliably resolve extremes at high spatial scales. Close

• 3. [Article] High-Performance Computing for Drought Prediction

  In recent decades, there has been considerable interest in using satellite soil moisture data to examine the global water-energy cycle and manage water resources. Current satellites are limited in their ...

  Citation × Citation Citation Abstract Copy Title: High-Performance Computing for Drought Prediction Author: Cooney, Henry Year: 2016 In recent decades, there has been considerable interest in using satellite soil moisture data to examine the global water-energy cycle and manage water resources. Current satellites are limited in their sensing depth, and can only directly measure top soil layers. Using a particle filter, this data may be fused with the output of a hydrologic simulation to improve simulation results, and characterize a hydrologic system at the watershed level. However, this approach increases computational requirements dramatically, and requires rethinking to accommodate data scaling and achieve good performance. We present a detailed performance study of several alternative implementations of the hybrid approach. We combined the Precipitation Runoff Modeling System (PRMS), a widely used hydrologic model published by the U.S. Geologic Survey, with satellite data. This problem is representative of many science efforts in that it incorporates software used across the scientific domain (PRMS) with novel functionality by integrating satellite data. Title: High-Performance Computing for Drought Prediction Author: Cooney, Henry Year: 2016 In recent decades, there has been considerable interest in using satellite soil moisture data to examine the global water-energy cycle and manage water resources. Current satellites are limited in their sensing depth, and can only directly measure top soil layers. Using a particle filter, this data may be fused with the output of a hydrologic simulation to improve simulation results, and characterize a hydrologic system at the watershed level. However, this approach increases computational requirements dramatically, and requires rethinking to accommodate data scaling and achieve good performance. We present a detailed performance study of several alternative implementations of the hybrid approach. We combined the Precipitation Runoff Modeling System (PRMS), a widely used hydrologic model published by the U.S. Geologic Survey, with satellite data. This problem is representative of many science efforts in that it incorporates software used across the scientific domain (PRMS) with novel functionality by integrating satellite data. Close

• 4. [Article] ENSO Controls Interannual Fire Activity in Southeast Australia

  El Niño–Southern Oscillation (ENSO) is the main mode controlling the variability in the ocean-atmosphere system in the South Pacific. While the ENSO influence on rainfall regimes in the South Pacific is ...

  Citation × Citation Citation Abstract Copy Title: ENSO Controls Interannual Fire Activity in Southeast Australia Author: Mariani, Michela, Fletcher, Michael-Shawn, Holz, Andrés, Nyman, Petter Year: 2016 El Niño–Southern Oscillation (ENSO) is the main mode controlling the variability in the ocean-atmosphere system in the South Pacific. While the ENSO influence on rainfall regimes in the South Pacific is well documented, its role in driving spatiotemporal trends in fire activity in this region has not been rigorously investigated. This is particularly the case for the highly flammable and densely populated southeast Australian sector, where ENSO is a major control over climatic variability. Here we conduct the first region-wide analysis of how ENSO controls fire activity in southeast Australia. We identify a significant relationship between ENSO and both fire frequency and area burnt. Critically, wavelet analyses reveal that despite substantial temporal variability in the ENSO system, ENSO exerts a persistent and significant influence on southeast Australian fire activity. Our analysis has direct application for developing robust predictive capacity for the increasingly important efforts at fire management. Title: ENSO Controls Interannual Fire Activity in Southeast Australia Author: Mariani, Michela, Fletcher, Michael-Shawn, Holz, Andrés, Nyman, Petter Year: 2016 El Niño–Southern Oscillation (ENSO) is the main mode controlling the variability in the ocean-atmosphere system in the South Pacific. While the ENSO influence on rainfall regimes in the South Pacific is well documented, its role in driving spatiotemporal trends in fire activity in this region has not been rigorously investigated. This is particularly the case for the highly flammable and densely populated southeast Australian sector, where ENSO is a major control over climatic variability. Here we conduct the first region-wide analysis of how ENSO controls fire activity in southeast Australia. We identify a significant relationship between ENSO and both fire frequency and area burnt. Critically, wavelet analyses reveal that despite substantial temporal variability in the ENSO system, ENSO exerts a persistent and significant influence on southeast Australian fire activity. Our analysis has direct application for developing robust predictive capacity for the increasingly important efforts at fire management. Close

• 5. [Article] Sensitivity of CONUS Summer Rainfall to the Selection of Cumulus Parameterization Schemes in NU-WRF Seasonal Simulations

  This study investigates the sensitivity of daily rainfall rates in regional seasonal simulations over the contiguous United States (CONUS) to different cumulus parameterization schemes. Daily rainfall ...

  Citation × Citation Citation Abstract Copy Title: Sensitivity of CONUS Summer Rainfall to the Selection of Cumulus Parameterization Schemes in NU-WRF Seasonal Simulations Author: Iguchia, Takamichi, Tao, Wei-Kuo, Wu, Di, Peters-Lidard, Christa D., Santanello, Joseph A., Kemp, Eric, Tian, Yudong, Case, Jonathan, Wang, Weile, Ferraro, Robert, Waliser, Duane E., Kim, Jinwon, Lee, Huikyo, Guan, Bin, Tian, Baijun, Loikith, Paul C. Year: 2017 This study investigates the sensitivity of daily rainfall rates in regional seasonal simulations over the contiguous United States (CONUS) to different cumulus parameterization schemes. Daily rainfall fields were simulated at 24-km resolution using the NASA-Unified Weather Research and Forecasting (NU-WRF) Model for June–August 2000. Four cumulus parameterization schemes and two options for shallow cumulus components in a specific scheme were tested. The spread in the domain-mean rainfall rates across the parameterization schemes was generally consistent between the entire CONUS and most subregions. The selection of the shallow cumulus component in a specific scheme had more impact than that of the four cumulus parameterization schemes. Regional variability in the performance of each scheme was assessed by calculating optimally weighted ensembles that minimize full root-mean-square errors against reference datasets. The spatial pattern of the seasonally averaged rainfall was insensitive to the selection of cumulus parameterization over mountainous regions because of the topographical pattern constraint, so that the simulation errors were mostly attributed to the overall bias there. In contrast, the spatial patterns over the Great Plains regions as well as the temporal variation over most parts of the CONUS were relatively sensitive to cumulus parameterization selection. Overall, adopting a single simulation result was preferable to generating a better ensemble for the seasonally averaged daily rainfall simulation, as long as their overall biases had the same positive or negative sign. However, an ensemble of multiple simulation results was more effective in reducing errors in the case of also considering temporal variation. Title: Sensitivity of CONUS Summer Rainfall to the Selection of Cumulus Parameterization Schemes in NU-WRF Seasonal Simulations Author: Iguchia, Takamichi, Tao, Wei-Kuo, Wu, Di, Peters-Lidard, Christa D., Santanello, Joseph A., Kemp, Eric, Tian, Yudong, Case, Jonathan, Wang, Weile, Ferraro, Robert, Waliser, Duane E., Kim, Jinwon, Lee, Huikyo, Guan, Bin, Tian, Baijun, Loikith, Paul C. Year: 2017 This study investigates the sensitivity of daily rainfall rates in regional seasonal simulations over the contiguous United States (CONUS) to different cumulus parameterization schemes. Daily rainfall fields were simulated at 24-km resolution using the NASA-Unified Weather Research and Forecasting (NU-WRF) Model for June–August 2000. Four cumulus parameterization schemes and two options for shallow cumulus components in a specific scheme were tested. The spread in the domain-mean rainfall rates across the parameterization schemes was generally consistent between the entire CONUS and most subregions. The selection of the shallow cumulus component in a specific scheme had more impact than that of the four cumulus parameterization schemes. Regional variability in the performance of each scheme was assessed by calculating optimally weighted ensembles that minimize full root-mean-square errors against reference datasets. The spatial pattern of the seasonally averaged rainfall was insensitive to the selection of cumulus parameterization over mountainous regions because of the topographical pattern constraint, so that the simulation errors were mostly attributed to the overall bias there. In contrast, the spatial patterns over the Great Plains regions as well as the temporal variation over most parts of the CONUS were relatively sensitive to cumulus parameterization selection. Overall, adopting a single simulation result was preferable to generating a better ensemble for the seasonally averaged daily rainfall simulation, as long as their overall biases had the same positive or negative sign. However, an ensemble of multiple simulation results was more effective in reducing errors in the case of also considering temporal variation. Close