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- Author or Editor: Yu-Fen Huang x
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Abstract
The seasonal variations of rainfall over the island of Hawaii are studied using the archives of the daily model run from the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) from June 2004 to February 2010. Local effects mainly drive the rainfall on the Kona coast in the early morning and the lower slopes in the afternoon. During the summer, the incoming trade winds are more persistent and moister than in winter. The moisture content in the wake zone is higher than open-ocean values because of the convergent airflow associated with dual counterrotating vortices. As the westerly reversed flow moves toward the Kona coast, it decelerates with increasing moisture and a moisture maximum over the coastal area, especially in the afternoon hours in summer months. The higher afternoon rainfall on the Kona lower slopes in summer than in winter is caused by a moister (>6 mm) westerly reversed flow bringing moisture inland and merging with a stronger upslope flow resulting from solar heating. Higher nocturnal rainfall off the Kona coast in summer than in winter is caused by the low-level convergence between a moister westerly reversed flow and offshore flow. On the windward slopes, the simulated rainfall accumulation in winter is higher because of frequently occurring synoptic disturbances during the winter storm season. Nevertheless, early morning rainfall along the windward coast and afternoon rainfall over the windward slopes of the Kohala Mountains is lower in winter because the incoming trades are drier.
Abstract
The seasonal variations of rainfall over the island of Hawaii are studied using the archives of the daily model run from the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) from June 2004 to February 2010. Local effects mainly drive the rainfall on the Kona coast in the early morning and the lower slopes in the afternoon. During the summer, the incoming trade winds are more persistent and moister than in winter. The moisture content in the wake zone is higher than open-ocean values because of the convergent airflow associated with dual counterrotating vortices. As the westerly reversed flow moves toward the Kona coast, it decelerates with increasing moisture and a moisture maximum over the coastal area, especially in the afternoon hours in summer months. The higher afternoon rainfall on the Kona lower slopes in summer than in winter is caused by a moister (>6 mm) westerly reversed flow bringing moisture inland and merging with a stronger upslope flow resulting from solar heating. Higher nocturnal rainfall off the Kona coast in summer than in winter is caused by the low-level convergence between a moister westerly reversed flow and offshore flow. On the windward slopes, the simulated rainfall accumulation in winter is higher because of frequently occurring synoptic disturbances during the winter storm season. Nevertheless, early morning rainfall along the windward coast and afternoon rainfall over the windward slopes of the Kohala Mountains is lower in winter because the incoming trades are drier.
Abstract
High temporal and spatial resolution precipitation datasets are essential for hydrological and flood modeling to assist water resource management and emergency responses, particularly for small watersheds, such as those in Hawai‘i in the United States. Unfortunately, fine temporal (subdaily) and spatial (<1 km) resolutions of rainfall datasets are not always readily available for applications. Radar provides indirect measurements of the rain rate over a large spatial extent with a reasonable temporal resolution, while rain gauges provide “ground truth.” There are potential advantages to combining the two, which have not been fully explored in tropical islands. In this study, we applied kriging with external drift (KED) to integrate hourly gauge and radar rainfall into a 250 m × 250 m gridded dataset for the tropical island of O‘ahu. The results were validated with leave-one-out cross validation for 18 severe storm events, including five different storm types (e.g., tropical cyclone, cold front, upper-level trough, kona low, and a mix of upper-level trough and kona low), and different rainfall structures (e.g., stratiform and convective). KED-merged rainfall estimates outperformed both the radar-only and gauge-only datasets by 1) reducing the error from radar rainfall and 2) improving the underestimation issues from gauge rainfall, especially during convective rainfall. We confirmed the KED method can be used to merge radar with gauge data to generate reliable rainfall estimates, particularly for storm events, on mountainous tropical islands. In addition, KED rainfall estimates were consistently more accurate in depicting spatial distribution and maximum rainfall value within various storm types and rainfall structures.
Significance Statement
The results of this study show the effectiveness of utilizing kriging with external drift (KED) in merging gauge and radar rainfall data to produce highly accurate, reliable rainfall estimates in mountainous tropical regions, such as O‘ahu. The validated KED dataset, with its high temporal and spatial resolutions, offers a valuable resource for various types of rainfall-related research, particularly for extreme weather response and rainfall intensity analyses in Hawai’i. Our findings improve the accuracy of rainfall estimates and contribute to a deeper understanding of the performance of various rainfall estimation methods under different storm types and rainfall structures in a mountainous tropical setting.
Abstract
High temporal and spatial resolution precipitation datasets are essential for hydrological and flood modeling to assist water resource management and emergency responses, particularly for small watersheds, such as those in Hawai‘i in the United States. Unfortunately, fine temporal (subdaily) and spatial (<1 km) resolutions of rainfall datasets are not always readily available for applications. Radar provides indirect measurements of the rain rate over a large spatial extent with a reasonable temporal resolution, while rain gauges provide “ground truth.” There are potential advantages to combining the two, which have not been fully explored in tropical islands. In this study, we applied kriging with external drift (KED) to integrate hourly gauge and radar rainfall into a 250 m × 250 m gridded dataset for the tropical island of O‘ahu. The results were validated with leave-one-out cross validation for 18 severe storm events, including five different storm types (e.g., tropical cyclone, cold front, upper-level trough, kona low, and a mix of upper-level trough and kona low), and different rainfall structures (e.g., stratiform and convective). KED-merged rainfall estimates outperformed both the radar-only and gauge-only datasets by 1) reducing the error from radar rainfall and 2) improving the underestimation issues from gauge rainfall, especially during convective rainfall. We confirmed the KED method can be used to merge radar with gauge data to generate reliable rainfall estimates, particularly for storm events, on mountainous tropical islands. In addition, KED rainfall estimates were consistently more accurate in depicting spatial distribution and maximum rainfall value within various storm types and rainfall structures.
Significance Statement
The results of this study show the effectiveness of utilizing kriging with external drift (KED) in merging gauge and radar rainfall data to produce highly accurate, reliable rainfall estimates in mountainous tropical regions, such as O‘ahu. The validated KED dataset, with its high temporal and spatial resolutions, offers a valuable resource for various types of rainfall-related research, particularly for extreme weather response and rainfall intensity analyses in Hawai’i. Our findings improve the accuracy of rainfall estimates and contribute to a deeper understanding of the performance of various rainfall estimation methods under different storm types and rainfall structures in a mountainous tropical setting.
Abstract
Gridded monthly rainfall estimates can be used for a number of research applications, including hydrologic modeling and weather forecasting. Automated interpolation algorithms, such as the “autoKrige” function in R, can produce gridded rainfall estimates that validate well but produce unrealistic spatial patterns. In this work, an optimized geostatistical kriging approach is used to interpolate relative rainfall anomalies, which are then combined with long-term means to develop the gridded estimates. The optimization consists of the following: 1) determining the most appropriate offset (constant) to use when log-transforming data; 2) eliminating poor quality data prior to interpolation; 3) detecting erroneous maps using a machine learning algorithm; and 4) selecting the most appropriate parameterization scheme for fitting the model used in the interpolation. Results of this effort include a 30-yr (1990–2019), high-resolution (250-m) gridded monthly rainfall time series for the state of Hawai‘i. Leave-one-out cross validation (LOOCV) is performed using an extensive network of 622 observation stations. LOOCV results are in good agreement with observations (R 2 = 0.78; MAE = 55 mm month−1; 1.4%); however, predictions can underestimate high rainfall observations (bias = 34 mm month−1; −1%) due to a well-known smoothing effect that occurs with kriging. This research highlights the fact that validation statistics should not be the sole source of error assessment and that default parameterizations for automated interpolation may need to be modified to produce realistic gridded rainfall surfaces. Data products can be accessed through the Hawai‘i Data Climate Portal (HCDP; http://www.hawaii.edu/climate-data-portal).
Significance Statement
A new method is developed to map rainfall in Hawai‘i using an optimized geostatistical kriging approach. A machine learning technique is used to detect erroneous rainfall maps and several conditions are implemented to select the optimal parameterization scheme for fitting the model used in the kriging interpolation. A key finding is that optimization of the interpolation approach is necessary because maps may validate well but have unrealistic spatial patterns. This approach demonstrates how, with a moderate amount of data, a low-level machine learning algorithm can be trained to evaluate and classify an unrealistic map output.
Abstract
Gridded monthly rainfall estimates can be used for a number of research applications, including hydrologic modeling and weather forecasting. Automated interpolation algorithms, such as the “autoKrige” function in R, can produce gridded rainfall estimates that validate well but produce unrealistic spatial patterns. In this work, an optimized geostatistical kriging approach is used to interpolate relative rainfall anomalies, which are then combined with long-term means to develop the gridded estimates. The optimization consists of the following: 1) determining the most appropriate offset (constant) to use when log-transforming data; 2) eliminating poor quality data prior to interpolation; 3) detecting erroneous maps using a machine learning algorithm; and 4) selecting the most appropriate parameterization scheme for fitting the model used in the interpolation. Results of this effort include a 30-yr (1990–2019), high-resolution (250-m) gridded monthly rainfall time series for the state of Hawai‘i. Leave-one-out cross validation (LOOCV) is performed using an extensive network of 622 observation stations. LOOCV results are in good agreement with observations (R 2 = 0.78; MAE = 55 mm month−1; 1.4%); however, predictions can underestimate high rainfall observations (bias = 34 mm month−1; −1%) due to a well-known smoothing effect that occurs with kriging. This research highlights the fact that validation statistics should not be the sole source of error assessment and that default parameterizations for automated interpolation may need to be modified to produce realistic gridded rainfall surfaces. Data products can be accessed through the Hawai‘i Data Climate Portal (HCDP; http://www.hawaii.edu/climate-data-portal).
Significance Statement
A new method is developed to map rainfall in Hawai‘i using an optimized geostatistical kriging approach. A machine learning technique is used to detect erroneous rainfall maps and several conditions are implemented to select the optimal parameterization scheme for fitting the model used in the kriging interpolation. A key finding is that optimization of the interpolation approach is necessary because maps may validate well but have unrealistic spatial patterns. This approach demonstrates how, with a moderate amount of data, a low-level machine learning algorithm can be trained to evaluate and classify an unrealistic map output.
