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Abstract
This study compares aerosol direct radiative effects on numerical weather forecasts made by the NCEP Global Forecast System (GFS) with two different aerosol datasets, the Optical Properties of Aerosols and Clouds (OPAC) and MERRA-2 aerosol climatologies. The underestimation of aerosol optical depth (AOD) by OPAC over northwest Africa, central to East Africa, the Arabian Peninsula, Southeast Asia, and the Indo-Gangetic Plain, and overestimation in the storm-track regions in both hemispheres are reduced by MERRA-2. Surface downward shortwave (SW) and longwave (LW) fluxes and the top-of-the-atmosphere SW and outgoing LW fluxes from model forecasts are compared with CERES satellite observations. Forecasts made with OPAC aerosols have large radiative flux biases, especially in northwest Africa and the storm-track regions. These biases are also reduced in the forecasts made with MERRA-2 aerosols. The improvements from MERRA-2 are most noticeable in the surface downward SW fluxes. GFS medium-range weather forecasts made with the MERRA-2 aerosols demonstrated slightly improved forecast accuracy of sea level pressure and precipitation over the Indian and East Asian summer monsoon region. A stronger Africa easterly jet is produced, associated with a low pressure over the east Atlantic Ocean and west of northwest Africa. Impacts on large-scale skill scores such as 500-hPa geopotential height anomaly correlation are generally positive in the Northern Hemisphere and the Pacific and North American regions in both the winter and summer seasons.
Abstract
This study compares aerosol direct radiative effects on numerical weather forecasts made by the NCEP Global Forecast System (GFS) with two different aerosol datasets, the Optical Properties of Aerosols and Clouds (OPAC) and MERRA-2 aerosol climatologies. The underestimation of aerosol optical depth (AOD) by OPAC over northwest Africa, central to East Africa, the Arabian Peninsula, Southeast Asia, and the Indo-Gangetic Plain, and overestimation in the storm-track regions in both hemispheres are reduced by MERRA-2. Surface downward shortwave (SW) and longwave (LW) fluxes and the top-of-the-atmosphere SW and outgoing LW fluxes from model forecasts are compared with CERES satellite observations. Forecasts made with OPAC aerosols have large radiative flux biases, especially in northwest Africa and the storm-track regions. These biases are also reduced in the forecasts made with MERRA-2 aerosols. The improvements from MERRA-2 are most noticeable in the surface downward SW fluxes. GFS medium-range weather forecasts made with the MERRA-2 aerosols demonstrated slightly improved forecast accuracy of sea level pressure and precipitation over the Indian and East Asian summer monsoon region. A stronger Africa easterly jet is produced, associated with a low pressure over the east Atlantic Ocean and west of northwest Africa. Impacts on large-scale skill scores such as 500-hPa geopotential height anomaly correlation are generally positive in the Northern Hemisphere and the Pacific and North American regions in both the winter and summer seasons.
Abstract
A global multimodel probabilistic subseasonal forecast system for precipitation and near-surface temperature is developed based on three NOAA ensemble prediction systems that make their forecasts available publicly in real time as part of the Subseasonal eXperiment (SubX). The weekly and biweekly ensemble means of precipitation and temperature of each model are individually calibrated at each gridpoint using extended logistic regression, prior to forming equal-weighted multimodel ensemble (MME) probabilistic forecasts. Reforecast skill of weeks 3–4 precipitation and temperature is assessed in terms of the cross-validated ranked probability skill score (RPSS) and reliability diagram. The multimodel reforecasts are shown to be well-calibrated for both variables. Precipitation is moderately skillful over many tropical land regions, including Latin America, Sub-Saharan Africa and SE Asia, and over subtropical South America, Africa, and Australia. Near surface temperature skill is considerably higher than for precipitation and extends into the extratropics as well. The multimodel RPSS skill of both precipitation and temperature is shown to exceed that of any of the constituent models over Indonesia, South Asia, South America and East Africa, in all seasons. An example real-time weeks 3–4 global forecast for 13–26 November 2021 is illustrated and shown to bear the hallmarks of the combined influences of a moderate Madden-Julian Oscillation event as well as weak-moderate ongoing La Niña event.
Abstract
A global multimodel probabilistic subseasonal forecast system for precipitation and near-surface temperature is developed based on three NOAA ensemble prediction systems that make their forecasts available publicly in real time as part of the Subseasonal eXperiment (SubX). The weekly and biweekly ensemble means of precipitation and temperature of each model are individually calibrated at each gridpoint using extended logistic regression, prior to forming equal-weighted multimodel ensemble (MME) probabilistic forecasts. Reforecast skill of weeks 3–4 precipitation and temperature is assessed in terms of the cross-validated ranked probability skill score (RPSS) and reliability diagram. The multimodel reforecasts are shown to be well-calibrated for both variables. Precipitation is moderately skillful over many tropical land regions, including Latin America, Sub-Saharan Africa and SE Asia, and over subtropical South America, Africa, and Australia. Near surface temperature skill is considerably higher than for precipitation and extends into the extratropics as well. The multimodel RPSS skill of both precipitation and temperature is shown to exceed that of any of the constituent models over Indonesia, South Asia, South America and East Africa, in all seasons. An example real-time weeks 3–4 global forecast for 13–26 November 2021 is illustrated and shown to bear the hallmarks of the combined influences of a moderate Madden-Julian Oscillation event as well as weak-moderate ongoing La Niña event.
Abstract
The Rapid Intensification Deterministic Ensemble (RIDE) is an operational method used to estimate the probability of tropical cyclone rapid intensification in the Joint Typhoon Warning Center’s Area of Responsibility. Inputs to RIDE are current intensity, storm latitude, intensity change forecasts from seven routinely available operational deterministic models of intensity change, and the number of those models exceeding their individual 90th percentile of intensity change. Deterministic model inputs come from four numerical weather prediction models, two statistical–dynamical models, and one purely statistical model. In RIDE, logistic regression combines the deterministic inputs to form a probabilistic rapid intensification forecast model. RIDE then also generates deterministic intensity forecasts from these probabilistic forecasts that serve as forecaster guidance and as input to intensity consensus aids. Results based on a year of independent verification suggest good reliability and discrimination with a general tendency to underpredict rapid intensification events, but with few false alarms.
Abstract
The Rapid Intensification Deterministic Ensemble (RIDE) is an operational method used to estimate the probability of tropical cyclone rapid intensification in the Joint Typhoon Warning Center’s Area of Responsibility. Inputs to RIDE are current intensity, storm latitude, intensity change forecasts from seven routinely available operational deterministic models of intensity change, and the number of those models exceeding their individual 90th percentile of intensity change. Deterministic model inputs come from four numerical weather prediction models, two statistical–dynamical models, and one purely statistical model. In RIDE, logistic regression combines the deterministic inputs to form a probabilistic rapid intensification forecast model. RIDE then also generates deterministic intensity forecasts from these probabilistic forecasts that serve as forecaster guidance and as input to intensity consensus aids. Results based on a year of independent verification suggest good reliability and discrimination with a general tendency to underpredict rapid intensification events, but with few false alarms.
Abstract
This study employs a long time series (1997–2017) of reforecasts based on a version of the ECMWF Integrated Forecast System to evaluate the dependence of medium-range (i.e., 3–15 days) precipitation forecast skill over California on the state of the large-scale atmospheric flow. As a basis for this evaluation, four recurrent large-scale flow regimes over the North Pacific and western North America associated with precipitation in a domain encompassing northern and central California were objectively identified in ECMWF ERA5 reanalysis data for November–March 1981–2017. Two of the regimes are characterized by zonal upper-level flow across the North Pacific, and the other two are characterized by wavy, blocked flow. Forecast verification statistics conditioned on regime occurrence indicate considerably lower medium-range precipitation skill over California in blocking regimes than in zonal regimes. Moreover, forecasts of blocking regimes tend to exhibit larger errors and uncertainty in the synoptic-scale flow over the eastern North Pacific and western North America compared with forecasts of zonal regimes. Composite analyses for blocking forecasts reveal a tendency for errors to develop in conjunction with the amplification of a ridge over the western and central North Pacific. The errors in the ridge tend to be communicated through the large-scale Rossby wave pattern, resulting in misforecasting of downstream trough amplification and, thereby, moisture flux and precipitation over California. The composites additionally indicate that error growth in the blocking ridge can be linked to misrepresentation of baroclinic development as well as upper-level divergent outflow associated with latent heat release.
Significance Statement
This study examines the degree to which the medium-range (out to ∼2-week lead time) precipitation forecast skill over California depends on the large-scale atmospheric flow regime over the North Pacific. An evaluation of retrospective model forecasts from ECMWF for 1997–2017 reveals that the skill tends to be considerably lower in regimes featuring a wavy, “blocked” North Pacific jet stream than in regimes featuring a west–east-oriented jet stream. This difference in skill relates to a tendency for forecasts of blocked regimes to exhibit significantly larger errors than forecasts of zonal regimes. The results could aid forecasters by increasing situational awareness and informing the interpretation and application of model forecasts for precipitation affecting California.
Abstract
This study employs a long time series (1997–2017) of reforecasts based on a version of the ECMWF Integrated Forecast System to evaluate the dependence of medium-range (i.e., 3–15 days) precipitation forecast skill over California on the state of the large-scale atmospheric flow. As a basis for this evaluation, four recurrent large-scale flow regimes over the North Pacific and western North America associated with precipitation in a domain encompassing northern and central California were objectively identified in ECMWF ERA5 reanalysis data for November–March 1981–2017. Two of the regimes are characterized by zonal upper-level flow across the North Pacific, and the other two are characterized by wavy, blocked flow. Forecast verification statistics conditioned on regime occurrence indicate considerably lower medium-range precipitation skill over California in blocking regimes than in zonal regimes. Moreover, forecasts of blocking regimes tend to exhibit larger errors and uncertainty in the synoptic-scale flow over the eastern North Pacific and western North America compared with forecasts of zonal regimes. Composite analyses for blocking forecasts reveal a tendency for errors to develop in conjunction with the amplification of a ridge over the western and central North Pacific. The errors in the ridge tend to be communicated through the large-scale Rossby wave pattern, resulting in misforecasting of downstream trough amplification and, thereby, moisture flux and precipitation over California. The composites additionally indicate that error growth in the blocking ridge can be linked to misrepresentation of baroclinic development as well as upper-level divergent outflow associated with latent heat release.
Significance Statement
This study examines the degree to which the medium-range (out to ∼2-week lead time) precipitation forecast skill over California depends on the large-scale atmospheric flow regime over the North Pacific. An evaluation of retrospective model forecasts from ECMWF for 1997–2017 reveals that the skill tends to be considerably lower in regimes featuring a wavy, “blocked” North Pacific jet stream than in regimes featuring a west–east-oriented jet stream. This difference in skill relates to a tendency for forecasts of blocked regimes to exhibit significantly larger errors than forecasts of zonal regimes. The results could aid forecasters by increasing situational awareness and informing the interpretation and application of model forecasts for precipitation affecting California.
Abstract
The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical weather forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the National Centers for Environmental Prediction (NCEP) Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble–variational (4DEnVar) data assimilation system. The control run (CTRL) used all of the routinely assimilated data and included data from 628 ARR dropsondes, whereas the denial run (DENY) excluded the dropsonde data. Results from 17 intensive observing periods (IOPs) indicate a mixed impact for mean sea level pressure and geopotential height over the Pacific–North American (PNA) region in CTRL compared to DENY. The overall local impact over the U.S. West Coast and Gulf of Alaska for the 17 IOPs is neutral (−0.45%) for integrated vapor transport (IVT), but positive for wind and moisture profiles (0.5%–1.0%), with a spectrum of statistically significant positive and negative impacts for various IOPs. The positive dropsonde data impact on precipitation forecasts over U.S. West Coast domains appears driven, in part, by improved low-level moisture and wind fields at short-forecast lead times. Indeed, data gaps, especially for accurate and unbiased moisture profiles and wind fields, can be at least partially mitigated to improve U.S. West Coast precipitation forecasts.
Abstract
The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical weather forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the National Centers for Environmental Prediction (NCEP) Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble–variational (4DEnVar) data assimilation system. The control run (CTRL) used all of the routinely assimilated data and included data from 628 ARR dropsondes, whereas the denial run (DENY) excluded the dropsonde data. Results from 17 intensive observing periods (IOPs) indicate a mixed impact for mean sea level pressure and geopotential height over the Pacific–North American (PNA) region in CTRL compared to DENY. The overall local impact over the U.S. West Coast and Gulf of Alaska for the 17 IOPs is neutral (−0.45%) for integrated vapor transport (IVT), but positive for wind and moisture profiles (0.5%–1.0%), with a spectrum of statistically significant positive and negative impacts for various IOPs. The positive dropsonde data impact on precipitation forecasts over U.S. West Coast domains appears driven, in part, by improved low-level moisture and wind fields at short-forecast lead times. Indeed, data gaps, especially for accurate and unbiased moisture profiles and wind fields, can be at least partially mitigated to improve U.S. West Coast precipitation forecasts.
Abstract
Inland flooding from landfalling tropical cyclones (TCs) is a major cause of death and damage to property and infrastructure worldwide. The mid-Atlantic region of the United States was devastated by Hurricane Irene and Tropical Storm Lee during late August–early September 2011, when the two storms produced sequential heavy rainfall and record flooding. Many rivers and streams reached their all-time record discharge to date. This study aims at 1) better understanding and predicting TC rainfall using various observed rainfall products and a high-resolution coupled atmosphere–wave–ocean model, namely, the Unified Wave Interface-Coupled Model (UWIN-CM), 2) characterizing inland flooding using streamflow data, and 3) improving prediction of TC-induced inland flooding using UWIN-CM and a machine learning K-nearest-neighbor (KNN) model. The results show that there is a wide range of uncertainty in satellite and radar–gauge-observed rainfall products in terms of rain-rate distribution and cumulative rainfall over the mid-Atlantic region. UWIN-CM rainfall is closer to the radar–gauge data than satellite data over land. Streamflow in most large rivers (>500 cfs) peaked after Lee, which reflects the sequential rainfall contributions of the two storms. The rainfall–streamflow–discharge response times were dependent on the size of the stream and the peak rain rates. To better predict rainfall and flooding, UWIN-CM and observed rainfall are used with the machine learning KNN regression model for prediction of severity of TC-induced inland flooding hazard. These results demonstrate the value of a stepped approach for rainfall and flood prediction toward a fully coupled atmosphere–ocean–land/hydrology model in the future.
Abstract
Inland flooding from landfalling tropical cyclones (TCs) is a major cause of death and damage to property and infrastructure worldwide. The mid-Atlantic region of the United States was devastated by Hurricane Irene and Tropical Storm Lee during late August–early September 2011, when the two storms produced sequential heavy rainfall and record flooding. Many rivers and streams reached their all-time record discharge to date. This study aims at 1) better understanding and predicting TC rainfall using various observed rainfall products and a high-resolution coupled atmosphere–wave–ocean model, namely, the Unified Wave Interface-Coupled Model (UWIN-CM), 2) characterizing inland flooding using streamflow data, and 3) improving prediction of TC-induced inland flooding using UWIN-CM and a machine learning K-nearest-neighbor (KNN) model. The results show that there is a wide range of uncertainty in satellite and radar–gauge-observed rainfall products in terms of rain-rate distribution and cumulative rainfall over the mid-Atlantic region. UWIN-CM rainfall is closer to the radar–gauge data than satellite data over land. Streamflow in most large rivers (>500 cfs) peaked after Lee, which reflects the sequential rainfall contributions of the two storms. The rainfall–streamflow–discharge response times were dependent on the size of the stream and the peak rain rates. To better predict rainfall and flooding, UWIN-CM and observed rainfall are used with the machine learning KNN regression model for prediction of severity of TC-induced inland flooding hazard. These results demonstrate the value of a stepped approach for rainfall and flood prediction toward a fully coupled atmosphere–ocean–land/hydrology model in the future.
Abstract
Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, postprocessing methods, from simple bias correction methods to more complicated multiple linear regression–based model output statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as week-3–4 forecasts. In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve week-3–4 precipitation and 2-m temperature forecasts made by the NOAA/NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and covariabilities hidden within the multidimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model week-3–4 precipitation and 2-m temperature forecasts. The results show that to some extent neural network techniques can significantly improve the week-3–4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.
Abstract
Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, postprocessing methods, from simple bias correction methods to more complicated multiple linear regression–based model output statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as week-3–4 forecasts. In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve week-3–4 precipitation and 2-m temperature forecasts made by the NOAA/NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and covariabilities hidden within the multidimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model week-3–4 precipitation and 2-m temperature forecasts. The results show that to some extent neural network techniques can significantly improve the week-3–4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.
Abstract
Tropical cyclone tornadoes (TCTORs) are a hazard to life and property during landfalling tropical cyclones (TCs). The threat is often spread over a wide area within the TC envelope and must be continually evaluated as the TC moves inland and dissipates. To anticipate the risk of TCTORs, forecasters may use high-resolution, rapidly updating model analyses and short-range forecasts such as the Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), and an ingredients-based approach similar to that used for forecasting continental midlatitude tornadoes. Though RAP and HRRR errors have been identified in typical midlatitude convective environments, this study evaluates the performance of the RAP and the HRRR within the TC envelope, with particular attention given to sounding-derived parameters previously identified as useful for TCTOR forecasting. A sample of 1730 observed upper-air soundings is sourced from 13 TCs that made landfall along the U.S. coastline between 2017 and 2019. The observed soundings are paired with their corresponding model gridpoint soundings from the RAP analysis, RAP 12-h forecast, and HRRR 12-h forecast. Model errors are calculated for both the raw sounding variables of temperature, dewpoint, and wind speed, as well as for the selected sounding-derived parameters. Results show a moist bias that worsens with height across all model runs. There are also statistically significant underpredictions in stability-related parameters such as convective available potential energy (CAPE) and kinematic parameters such as vertical wind shear.
Abstract
Tropical cyclone tornadoes (TCTORs) are a hazard to life and property during landfalling tropical cyclones (TCs). The threat is often spread over a wide area within the TC envelope and must be continually evaluated as the TC moves inland and dissipates. To anticipate the risk of TCTORs, forecasters may use high-resolution, rapidly updating model analyses and short-range forecasts such as the Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), and an ingredients-based approach similar to that used for forecasting continental midlatitude tornadoes. Though RAP and HRRR errors have been identified in typical midlatitude convective environments, this study evaluates the performance of the RAP and the HRRR within the TC envelope, with particular attention given to sounding-derived parameters previously identified as useful for TCTOR forecasting. A sample of 1730 observed upper-air soundings is sourced from 13 TCs that made landfall along the U.S. coastline between 2017 and 2019. The observed soundings are paired with their corresponding model gridpoint soundings from the RAP analysis, RAP 12-h forecast, and HRRR 12-h forecast. Model errors are calculated for both the raw sounding variables of temperature, dewpoint, and wind speed, as well as for the selected sounding-derived parameters. Results show a moist bias that worsens with height across all model runs. There are also statistically significant underpredictions in stability-related parameters such as convective available potential energy (CAPE) and kinematic parameters such as vertical wind shear.
