Abstract
There is a need for long term observations of cloud and precipitation fall speeds for validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility Southern Great Plains (SGP) site at Lamont, OK hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at ARM’s SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear air and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k- means clustering identifies ten clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud base heights.
Abstract
There is a need for long term observations of cloud and precipitation fall speeds for validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility Southern Great Plains (SGP) site at Lamont, OK hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at ARM’s SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear air and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k- means clustering identifies ten clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud base heights.
Abstract
Estimating the magnitude of tropical cyclone (TC) rainfall at different landfalling stages is an important aspect of the TC forecast that directly affects the level of response from emergency managers. In this study, a climatology of the TC rainfall magnitude as a function of the location of the TC centers within distance intervals from the coast and the percentage of the raining area over the land is presented on a global scale. A total of 1834 TCs in the period from 2000 until 2019 are analyzed using satellite information to characterize the precipitation magnitude, volumetric rain, rainfall area, and axial-symmetric properties within the proposed landfalling categories, with an emphasis on the post-landfall stages. We found that TCs experience rainfall maxima in regions adjacent to the coast when more than 50% of their rainfall area is over the water. TC rainfall is also analyzed over the entire TC extent and the portion over land. When the total extent is considered, rainfall intensity, volumetric rain, and rainfall area increase with wind speed intensity. However, once it is quantified over the land only, we found that rainfall intensity exhibits a nearly perfect inversely proportional relation with the increase in TC rainfall area. In addition, when a TC with life maximum intensity of a major hurricane makes landfall as a tropical depression or tropical storm, it usually produces the largest spatial extent and the highest volumetric rain.
Abstract
Estimating the magnitude of tropical cyclone (TC) rainfall at different landfalling stages is an important aspect of the TC forecast that directly affects the level of response from emergency managers. In this study, a climatology of the TC rainfall magnitude as a function of the location of the TC centers within distance intervals from the coast and the percentage of the raining area over the land is presented on a global scale. A total of 1834 TCs in the period from 2000 until 2019 are analyzed using satellite information to characterize the precipitation magnitude, volumetric rain, rainfall area, and axial-symmetric properties within the proposed landfalling categories, with an emphasis on the post-landfall stages. We found that TCs experience rainfall maxima in regions adjacent to the coast when more than 50% of their rainfall area is over the water. TC rainfall is also analyzed over the entire TC extent and the portion over land. When the total extent is considered, rainfall intensity, volumetric rain, and rainfall area increase with wind speed intensity. However, once it is quantified over the land only, we found that rainfall intensity exhibits a nearly perfect inversely proportional relation with the increase in TC rainfall area. In addition, when a TC with life maximum intensity of a major hurricane makes landfall as a tropical depression or tropical storm, it usually produces the largest spatial extent and the highest volumetric rain.
Abstract
Based on the hourly merged precipitation product, the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) in simulating the diurnal variations of precipitation during warm season over the western periphery of the SCB has been evaluated, and the underlying physical causes associated with the wet biases have also been investigated. The results show that the IFS well reproduces the spatial distributions of precipitation amount, frequency, intensity over the SCB, as well as their diurnal variations, but the simulated precipitation peaks earlier than the observation with notable wet biases over the western periphery of the SCB. In addition, the strong wet biases exhibit notable regional difference over the western periphery of the SCB. The simulated wet biases over the southwestern periphery of the SCB expanding westward to higher altitudes along the windward slope, with the maximum wet biases occurring at night. The westward expansion of the simulated stronger upward motions results in a westward shift of precipitation. However, the simulated precipitation over the northwestern periphery of the SCB have small difference in terms of the location, the overestimated precipitation is associated with the stronger atmospheric instability, resulting from the higher potential temperature and the larger specific humidity near the surface. The findings revealed in this study indicate that the ECMWF forecast shows distinct uncertainties over the different complex terrain, and thus offer a promising way forward for improvements of model physical processes.
Abstract
Based on the hourly merged precipitation product, the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) in simulating the diurnal variations of precipitation during warm season over the western periphery of the SCB has been evaluated, and the underlying physical causes associated with the wet biases have also been investigated. The results show that the IFS well reproduces the spatial distributions of precipitation amount, frequency, intensity over the SCB, as well as their diurnal variations, but the simulated precipitation peaks earlier than the observation with notable wet biases over the western periphery of the SCB. In addition, the strong wet biases exhibit notable regional difference over the western periphery of the SCB. The simulated wet biases over the southwestern periphery of the SCB expanding westward to higher altitudes along the windward slope, with the maximum wet biases occurring at night. The westward expansion of the simulated stronger upward motions results in a westward shift of precipitation. However, the simulated precipitation over the northwestern periphery of the SCB have small difference in terms of the location, the overestimated precipitation is associated with the stronger atmospheric instability, resulting from the higher potential temperature and the larger specific humidity near the surface. The findings revealed in this study indicate that the ECMWF forecast shows distinct uncertainties over the different complex terrain, and thus offer a promising way forward for improvements of model physical processes.
Abstract
Cities around the world are experiencing the effects of climate change via increasing extreme heat worsened by urbanization. Within cities, there are disparities in extreme heat exposure that are apparent in various surface and remotely-sensed observations, as well as in the health impacts. There are, however, large data gaps in our ability to quantify the heat experienced by people in their daily lives across urban areas. In this paper, we use hyperlocal observations to measure heat around Miami-Dade County. Temperature and humidity measurements were collected at sites throughout the county between 2018-2021 with low-cost sensors. By comparing these hyperlocal observations with a National Weather Service (NWS) site at the Miami International Airport (MIA), we show that maximum temperatures are on average 6°F higher, and maximum heat index values are 11°F higher, at sites in the county than at MIA. These measurements show that many sites frequently record a heat index above the local threshold value for heat advisory. This is in contrast with the fact that few forecast advisories are issued, and there are correspondingly few exceedances of the threshold at MIA. We use these results to motivate a discussion about the issues of this particular threshold for Miami-Dade County. We highlight the need for data that is closer to residents’ lived experience to assess the impacts of heat and help inform local and regional decision-making, particularly where heat exposure may be underappreciated as a potential public health hazard.
Abstract
Cities around the world are experiencing the effects of climate change via increasing extreme heat worsened by urbanization. Within cities, there are disparities in extreme heat exposure that are apparent in various surface and remotely-sensed observations, as well as in the health impacts. There are, however, large data gaps in our ability to quantify the heat experienced by people in their daily lives across urban areas. In this paper, we use hyperlocal observations to measure heat around Miami-Dade County. Temperature and humidity measurements were collected at sites throughout the county between 2018-2021 with low-cost sensors. By comparing these hyperlocal observations with a National Weather Service (NWS) site at the Miami International Airport (MIA), we show that maximum temperatures are on average 6°F higher, and maximum heat index values are 11°F higher, at sites in the county than at MIA. These measurements show that many sites frequently record a heat index above the local threshold value for heat advisory. This is in contrast with the fact that few forecast advisories are issued, and there are correspondingly few exceedances of the threshold at MIA. We use these results to motivate a discussion about the issues of this particular threshold for Miami-Dade County. We highlight the need for data that is closer to residents’ lived experience to assess the impacts of heat and help inform local and regional decision-making, particularly where heat exposure may be underappreciated as a potential public health hazard.
Abstract
Given the diversity of cloud forcing mechanisms, it is difficult to classify and characterize all cloud types through the depth of a specific troposphere. Importantly, different cloud families often coexist even at the same atmospheric level. The Naval Research Laboratory (NRL) is developing machine learning-based cloud forecast models to fuse numerical weather prediction model and satellite data. These models were built for the dual purpose of understanding numericalweather prediction model error trends aswell as improving the accuracy and sensitivity of the forecasts. The framework implements a Unet-Convolutional Neural Network (UNet-CNN) with features extracted from clouds observed by the Geostationary Operational Environmental Satellite (GOES-16) as well as clouds predicted by the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). The fundamental idea behind this novel framework is the application of UNet-CNN for separate variable sets extracted from GOES-16 and COAMPS to characterize and predict broad families of clouds that share similar physical characteristics. A quantitative assessment and evaluation based on an independent dataset for upper tropospheric (high) clouds suggests that UNet-CNN models capture the complexity and error trends of combined data from GOES-16 and COAMPS, and also improve forecast accuracy and sensitivity for different lead time forecasts (3-12 hours). This paper includes an overview of the machine learning frameworks as well as an illustrative example of their application, a comparative assessment of results for upper tropospheric clouds.
Abstract
Given the diversity of cloud forcing mechanisms, it is difficult to classify and characterize all cloud types through the depth of a specific troposphere. Importantly, different cloud families often coexist even at the same atmospheric level. The Naval Research Laboratory (NRL) is developing machine learning-based cloud forecast models to fuse numerical weather prediction model and satellite data. These models were built for the dual purpose of understanding numericalweather prediction model error trends aswell as improving the accuracy and sensitivity of the forecasts. The framework implements a Unet-Convolutional Neural Network (UNet-CNN) with features extracted from clouds observed by the Geostationary Operational Environmental Satellite (GOES-16) as well as clouds predicted by the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). The fundamental idea behind this novel framework is the application of UNet-CNN for separate variable sets extracted from GOES-16 and COAMPS to characterize and predict broad families of clouds that share similar physical characteristics. A quantitative assessment and evaluation based on an independent dataset for upper tropospheric (high) clouds suggests that UNet-CNN models capture the complexity and error trends of combined data from GOES-16 and COAMPS, and also improve forecast accuracy and sensitivity for different lead time forecasts (3-12 hours). This paper includes an overview of the machine learning frameworks as well as an illustrative example of their application, a comparative assessment of results for upper tropospheric clouds.
