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- Author or Editor: Laiyin Zhu x
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Abstract
Snow is an important component of Earth’s climate system, and snowfall intensity and variation often significantly impact society, the environment, and ecosystems. Understanding monthly and seasonal snowfall intensity and variations is challenging because of multiple controlling mechanisms at different spatial and temporal scales. Using 65 years of in situ snowfall observation, we evaluated seven machine learning algorithms for modeling monthly and seasonal snowfall in the Lower Peninsula of Michigan (LPM) based on selected environmental and climatic variables. Our results show that the Bayesian additive regression tree (BART) has the best fitting (R 2 = 0.88) and out-of-sample estimation skills (R 2 = 0.58) for the monthly mean snowfall followed by the random forest model. The BART also demonstrates strong estimation skills for large monthly snowfall amounts. Both BART and the random forest models suggest that topography, local/regional environmental factors, and teleconnection indices can significantly improve the estimation of monthly and seasonal snowfall amounts in the LPM. These statistical models based on machine learning algorithms can incorporate variables at multiple scales and address nonlinear responses of snowfall variations to environmental/climatic changes. It demonstrated that the multiscale machine learning techniques provide a reliable and computationally efficient approach to modeling snowfall intensity and variability.
Abstract
Snow is an important component of Earth’s climate system, and snowfall intensity and variation often significantly impact society, the environment, and ecosystems. Understanding monthly and seasonal snowfall intensity and variations is challenging because of multiple controlling mechanisms at different spatial and temporal scales. Using 65 years of in situ snowfall observation, we evaluated seven machine learning algorithms for modeling monthly and seasonal snowfall in the Lower Peninsula of Michigan (LPM) based on selected environmental and climatic variables. Our results show that the Bayesian additive regression tree (BART) has the best fitting (R 2 = 0.88) and out-of-sample estimation skills (R 2 = 0.58) for the monthly mean snowfall followed by the random forest model. The BART also demonstrates strong estimation skills for large monthly snowfall amounts. Both BART and the random forest models suggest that topography, local/regional environmental factors, and teleconnection indices can significantly improve the estimation of monthly and seasonal snowfall amounts in the LPM. These statistical models based on machine learning algorithms can incorporate variables at multiple scales and address nonlinear responses of snowfall variations to environmental/climatic changes. It demonstrated that the multiscale machine learning techniques provide a reliable and computationally efficient approach to modeling snowfall intensity and variability.
Abstract
Tropical cyclone precipitation (TCP) can cause significant flooding in coastal areas around the world. This study compares multiple options of a new technique for developing a gridded daily TCP product at a spatial resolution of 0.25°. These options were evaluated using NASA’s Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product to determine the optimal approach. Results indicate that the technique is very sensitive to changes in wind corrections, interpolation method, and gauge density. The optimal method accounts for wind-induced gauge undercatch and uses a customized interpolation approach. It significantly reduces precipitation biases associated with gauge undercatch during windy conditions. The new TCP extraction approach can be used to examine variability and long-term trends in TCP, even in regions with relatively few gauges.
Abstract
Tropical cyclone precipitation (TCP) can cause significant flooding in coastal areas around the world. This study compares multiple options of a new technique for developing a gridded daily TCP product at a spatial resolution of 0.25°. These options were evaluated using NASA’s Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product to determine the optimal approach. Results indicate that the technique is very sensitive to changes in wind corrections, interpolation method, and gauge density. The optimal method accounts for wind-induced gauge undercatch and uses a customized interpolation approach. It significantly reduces precipitation biases associated with gauge undercatch during windy conditions. The new TCP extraction approach can be used to examine variability and long-term trends in TCP, even in regions with relatively few gauges.
Abstract
Tropical cyclones pose a significant flood risk to vast land regions in their path because of extreme precipitation. Thus it is imperative to quantitatively assess this risk. This study compares exceedance frequencies of tropical cyclone precipitation derived from two independent observational datasets with those estimated using a tropical cyclone rainfall algorithm applied to large sets of synthetic tropical cyclones. The modeled rainfall compares reasonably well to observed rainfall across much of the southern United States but does less well in the mid-Atlantic states. Possible causes of this disparity are discussed.
Abstract
Tropical cyclones pose a significant flood risk to vast land regions in their path because of extreme precipitation. Thus it is imperative to quantitatively assess this risk. This study compares exceedance frequencies of tropical cyclone precipitation derived from two independent observational datasets with those estimated using a tropical cyclone rainfall algorithm applied to large sets of synthetic tropical cyclones. The modeled rainfall compares reasonably well to observed rainfall across much of the southern United States but does less well in the mid-Atlantic states. Possible causes of this disparity are discussed.
Abstract
Tropical cyclone precipitation (TCP) contributes a significant amount of precipitation each year in the contiguous United States and Mexico, and it can cause damaging floods. In this study, we evaluate the ability of two precipitation estimates from the latest Integrated Multisatellite Retrievals for GPM (IMERG Final Run V06, hereafter referred to as IMERG-F) and its predecessor, the TRMM Multisatellite Precipitation Analysis (TMPA research product 3B42V7, hereafter referred to as TMPA), to capture TCP at daily, event, and annual scales by comparing the satellite observations with gauge measurements based on data from 2014 to 2018. The results show that both TMPA and IMERG-F are able to accurately capture the general TCP patterns. IMERG-F provides a noticeable improvement in accuracy over TMPA, especially for times and locations with light and heavy TCP. However, both IMERG-F and TMPA still systematically underestimate TCP during extreme events. At the annual scale, both TMPA and IMERG-F slightly underestimate annual TCP, but IMERG-F to a lesser degree. For individual TC events, IMERG-F has lower bias and a higher Nash–Sutcliffe efficiency than TMPA in the majority of the events. The differences between IMERG-F and TMPA are especially pronounced for extreme TCP events, such as Hurricane Harvey in 2017. At the daily scale, both IMERG-F and TMPA underestimate TCP when daily TCP exceeds ~150 mm. However, IMERG-F shows closer agreements with gauge-based measurements than TMPA. This study demonstrates that IMERG-F can more accurately measure TCP than TMPA. However, there are still systematic biases in IMERG-F when it comes to heavy TCP at all of the time scales.
Abstract
Tropical cyclone precipitation (TCP) contributes a significant amount of precipitation each year in the contiguous United States and Mexico, and it can cause damaging floods. In this study, we evaluate the ability of two precipitation estimates from the latest Integrated Multisatellite Retrievals for GPM (IMERG Final Run V06, hereafter referred to as IMERG-F) and its predecessor, the TRMM Multisatellite Precipitation Analysis (TMPA research product 3B42V7, hereafter referred to as TMPA), to capture TCP at daily, event, and annual scales by comparing the satellite observations with gauge measurements based on data from 2014 to 2018. The results show that both TMPA and IMERG-F are able to accurately capture the general TCP patterns. IMERG-F provides a noticeable improvement in accuracy over TMPA, especially for times and locations with light and heavy TCP. However, both IMERG-F and TMPA still systematically underestimate TCP during extreme events. At the annual scale, both TMPA and IMERG-F slightly underestimate annual TCP, but IMERG-F to a lesser degree. For individual TC events, IMERG-F has lower bias and a higher Nash–Sutcliffe efficiency than TMPA in the majority of the events. The differences between IMERG-F and TMPA are especially pronounced for extreme TCP events, such as Hurricane Harvey in 2017. At the daily scale, both IMERG-F and TMPA underestimate TCP when daily TCP exceeds ~150 mm. However, IMERG-F shows closer agreements with gauge-based measurements than TMPA. This study demonstrates that IMERG-F can more accurately measure TCP than TMPA. However, there are still systematic biases in IMERG-F when it comes to heavy TCP at all of the time scales.
