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Understanding the Weather, Climate, and Vegetation Over Western North America : = Vegetation Phenology Monitoring, Short-Term Precipitation Prediction, and Seasonal Prediction of Temperature, Precipitation, and Snow Water Equivalent.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Understanding the Weather, Climate, and Vegetation Over Western North America :/
其他題名:	Vegetation Phenology Monitoring, Short-Term Precipitation Prediction, and Seasonal Prediction of Temperature, Precipitation, and Snow Water Equivalent.
作者:	Scheftic, William.
面頁冊數:	1 online resource (166 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
標題:	Atmospheric sciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30530988click for full text (PQDT)
ISBN:	9798379759384

Understanding the Weather, Climate, and Vegetation Over Western North America : = Vegetation Phenology Monitoring, Short-Term Precipitation Prediction, and Seasonal Prediction of Temperature, Precipitation, and Snow Water Equivalent.
Scheftic, William.

Understanding the Weather, Climate, and Vegetation Over Western North America :Vegetation Phenology Monitoring, Short-Term Precipitation Prediction, and Seasonal Prediction of Temperature, Precipitation, and Snow Water Equivalent. - 1 online resource (166 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--The University of Arizona, 2023.

Includes bibliographical references

The works presented here involve various facets of monitoring, understanding and forecasting seasonal variations of the weather and climate across western North America. This specifically includes long-term satellite-based monitoring of vegetation phenology, the post-processing of ensemble seasonal climate multi-models and generating downscaled and disaggregated ensemble precipitation forecasts for use in forcing short-term hydrologic models during the North American Monsoon season.Several satellite-derived Normalized Difference Vegetation Index (NDVI) products have been created that can be used to infer trends and interannual variability of vegetation and its phenology. In Scheftic et al. (2014), we evaluated four long-term NDVI datasets versus three shorter-term datasets based on newer sensors created more specifically for vegetation detection. While all datasets were well-correlated with each other, significant differences and temporal inconsistencies were found in regression slopes between the datasets. We found agreement in trends of higher maximum annual NDVI and a longer greening season over temperate forests of the U.S. and a lack of agreement over much of Mexico and the Southwest U.S. We computed the green vegetation fraction (GVF) from each NDVI dataset and found more one-to-one and temporally consistent relationships and weaker trends and interannual variations. The first implication is that studies should take care to validate conclusions about long-term changes in vegetation, especially phenology, with multiple datasets. Furthermore, GVF can be used to remove some temporal and spatial inconsistencies in these datasets, making conclusions on vegetation variability more robust.The inclusion of snowpack in Seasonal climate forecasts via snow water equivalent (SWE) can better serve water managers beyond the typical predictions of 2-m temperature (T2m) and precipitation (P). In Scheftic et al. (2023), we evaluated probabilistic seasonal forecasts of T2m, P and SWE across the western U.S. from two models (NCEP CFSv2 and ECMWF SEAS5). Specifically, we post-processed the forecasts from these models through a two-stage process that first removes model biases and then removes residual errors associated with antecedent information such as climate indices. The two models are then merged using prior skill. The first-stage bias correction improved the forecast skill of all three variables over the raw model forecasts, the second stage further improved SWE forecasts, and the merging of the models generally produced forecasts as skillful as the most skilled adjusted model. In particular, once biases and systematic model errors in snowpack initialization are removed, SWE can be forecasted even more skillfully than temperature or precipitation. The post-processing here was part of a seasonal forecasting system that was delivered to Idaho Power Company to create downscaled seasonal climate forecasts. We have also been producing winter forecasts for the past three winters over the western U.S. and selected regions of interest for the California Department of Water Resources and the 2022-23 forecasts are highlighted in DeFlorio et al. (2023) (including Scheftic as a coauthor).Uncertainty in the timing and location of precipitation during the North American Monsoon makes short-term flood likelihood forecasting very challenging. In Scheftic et al. (2013), we proposed a method of producing 24-hour probabilistic precipitation forecasts averaged over the Verde Basin using non-parametric joint PDFs and then disaggregating these forecasts into an ensemble of hourly temporal and 0.125° spatial resolution historical analogs. The results showed that the basin-averaged probability as well as the disaggregated analog forecasts were skillful relative to climatology. However, the mean and mode of the analog forecasts only showed deterministic skill comparable to the forecasting method that was used at the Colorado Basin River Forecast Center. An important contribution of this study is to provide a method of generating ensemble precipitation forcing for operational probabilistic short-term streamflow forecasting needed for predicting daily flood likelihoods.Contributions were also made to other studies. In Broxton et al. (2014) (including Scheftic as a coauthor), a 1 km global maximum green vegetation fraction dataset was created using 12 years of Moderate Resolution Imaging Spectrometer (MODIS) data using a very similar methodology that was used in producing GVF for Scheftic et al. (2014). The dataset was found to covary more with LAI than the MODIS Continuous Fields.Work is currently under way on three potential manuscripts. We have evaluated the usefulness of prior skill for merging multiple model ensembles of seasonal climate forecasts. It is found that commonly used methods of merging ensembles provide no significant improvement over weighting all models equally for six forecasting models across the western U.S. Work has also been done comparing empirical seasonal forecasts that use antecedent information from multiple time-lags through various analog and regression-based methods. Furthermore, Scheftic et al. (2013) was finished but not published, and we intend to further revise and update it for publication. Future work will focus on understanding the predictability and the sources of predictability at seasonal timescales for variables important to land hydrology across the conterminous U.S.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379759384Subjects--Topical Terms:

3168354
Atmospheric sciences.
Subjects--Index Terms:

SatelliteIndex Terms--Genre/Form:

542853
Electronic books.

Understanding the Weather, Climate, and Vegetation Over Western North America : = Vegetation Phenology Monitoring, Short-Term Precipitation Prediction, and Seasonal Prediction of Temperature, Precipitation, and Snow Water Equivalent.
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While all datasets were well-correlated with each other, significant differences and temporal inconsistencies were found in regression slopes between the datasets. We found agreement in trends of higher maximum annual NDVI and a longer greening season over temperate forests of the U.S. and a lack of agreement over much of Mexico and the Southwest U.S. We computed the green vegetation fraction (GVF) from each NDVI dataset and found more one-to-one and temporally consistent relationships and weaker trends and interannual variations. The first implication is that studies should take care to validate conclusions about long-term changes in vegetation, especially phenology, with multiple datasets. Furthermore, GVF can be used to remove some temporal and spatial inconsistencies in these datasets, making conclusions on vegetation variability more robust.The inclusion of snowpack in Seasonal climate forecasts via snow water equivalent (SWE) can better serve water managers beyond the typical predictions of 2-m temperature (T2m) and precipitation (P). In Scheftic et al. (2023), we evaluated probabilistic seasonal forecasts of T2m, P and SWE across the western U.S. from two models (NCEP CFSv2 and ECMWF SEAS5). Specifically, we post-processed the forecasts from these models through a two-stage process that first removes model biases and then removes residual errors associated with antecedent information such as climate indices. The two models are then merged using prior skill. The first-stage bias correction improved the forecast skill of all three variables over the raw model forecasts, the second stage further improved SWE forecasts, and the merging of the models generally produced forecasts as skillful as the most skilled adjusted model. In particular, once biases and systematic model errors in snowpack initialization are removed, SWE can be forecasted even more skillfully than temperature or precipitation. The post-processing here was part of a seasonal forecasting system that was delivered to Idaho Power Company to create downscaled seasonal climate forecasts. We have also been producing winter forecasts for the past three winters over the western U.S. and selected regions of interest for the California Department of Water Resources and the 2022-23 forecasts are highlighted in DeFlorio et al. (2023) (including Scheftic as a coauthor).Uncertainty in the timing and location of precipitation during the North American Monsoon makes short-term flood likelihood forecasting very challenging. In Scheftic et al. (2013), we proposed a method of producing 24-hour probabilistic precipitation forecasts averaged over the Verde Basin using non-parametric joint PDFs and then disaggregating these forecasts into an ensemble of hourly temporal and 0.125° spatial resolution historical analogs. The results showed that the basin-averaged probability as well as the disaggregated analog forecasts were skillful relative to climatology. However, the mean and mode of the analog forecasts only showed deterministic skill comparable to the forecasting method that was used at the Colorado Basin River Forecast Center. An important contribution of this study is to provide a method of generating ensemble precipitation forcing for operational probabilistic short-term streamflow forecasting needed for predicting daily flood likelihoods.Contributions were also made to other studies. In Broxton et al. (2014) (including Scheftic as a coauthor), a 1 km global maximum green vegetation fraction dataset was created using 12 years of Moderate Resolution Imaging Spectrometer (MODIS) data using a very similar methodology that was used in producing GVF for Scheftic et al. (2014). The dataset was found to covary more with LAI than the MODIS Continuous Fields.Work is currently under way on three potential manuscripts. We have evaluated the usefulness of prior skill for merging multiple model ensembles of seasonal climate forecasts. It is found that commonly used methods of merging ensembles provide no significant improvement over weighting all models equally for six forecasting models across the western U.S. Work has also been done comparing empirical seasonal forecasts that use antecedent information from multiple time-lags through various analog and regression-based methods. Furthermore, Scheftic et al. (2013) was finished but not published, and we intend to further revise and update it for publication. Future work will focus on understanding the predictability and the sources of predictability at seasonal timescales for variables important to land hydrology across the conterminous U.S.
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