Abstract
This study explores the impacts of assimilating all-sky infrared satellite radiances from Himawari-8, a new-generation geostationary satellite that shares similar remote sensing technology ...with the U.S. geostationary satellite GOES-16, for convection-permitting initialization and prediction of tropical cyclones with an ensemble Kalman filter (EnKF). This case studies the rapid intensification stages of Supertyphoon Soudelor (2015), one of the most intense tropical cyclones ever observed by Himawari-8. It is found that hourly cycling assimilation of the infrared radiance improves not only the estimate of the initial intensity, but also the spatial distribution of essential convective activity associated with the incipient tropical cyclone vortex. Deterministic convection-permitting forecasts initialized from the EnKF analyses are capable of simulating the early development of Soudelor, which demonstrates encouraging prospects for future improvement in tropical cyclone prediction through assimilating all-sky radiances from geostationary satellites such as Himawari-8 and GOES-16. A series of forecast sensitivity experiments are designed to systematically explore the impacts of moisture updates in the data assimilation cycles on the development and prediction of Soudelor. It is found that the assimilation of the brightness temperatures contributes not only to better constraining moist convection within the inner-core region, but also to developing a more resilient initial vortex, both of which are necessary to properly capture the rapid intensification process of tropical cyclones.
An empirical flow-dependent adaptive observation error inflation (AOEI) method is proposed for assimilating all-sky satellite brightness temperatures through observing system simulation experiments ...with an ensemble Kalman filter. The AOEI method adaptively inflates the observation error when the absolute difference (innovation) between the observed and simulated brightness temperatures is greater than the square root of the combined variance of the uninflated observational error variance and ensemble-estimated background error variance. This adaptive method is designed to limit erroneous analysis increments where there are large representativeness errors, as is often the case for cloudy-affected radiances, even if the forecast model and the observation operator (the radiative transfer model) are perfect. The promising performance of this newly proposed AOEI method is demonstrated through observing system simulation experiments assimilating all-sky brightness temperatures from GOES-R (now GOES-16) in comparison with experiments using an alternative empirical observation error inflation method proposed by Geer and Bauer. It is found that both inflation methods perform similarly in the accuracy of the analysis and in the containment of potential representativeness errors; both outperform experiments using a constant observation error without inflation. Besides being easier to implement, the empirical AOEI method proposed here also shows some advantage over the Geer–Bauer method in better updating variables at large scales. Large representative errors are likely to be compounded by unavoidable uncertainties in the forecast system and/or nonlinear observation operator (as for the radiative transfer model), in particular in the areas of moist processes, as will be the case for real-data cloudy radiances, which will be further investigated in future studies.
An adaptive background error inflation (ABEI) method is proposed for assimilating all‐sky satellite brightness temperatures with an ensemble Kalman filter. This empirical cloud‐scene‐dependent ...covariance inflation method is designed to mitigate the model's difficulties in initiating convection in the observed cloudy regions where the background prior estimated from the ensemble mean incorrectly simulates clear‐sky conditions. This new approach calculates a spatially varying, flow‐dependent, multiplicative ensemble covariance inflation factor based on error statistics produced by a well‐constructed, off‐line observing system simulation experiment (OSSE) that assimilates similar all‐sky radiance observations but were generated by the model, in which case the truth is known for all the state variables and the assimilated radiances. The adaptive inflation factor is a linear function of a cloud parameter which is only applied to the observed cloudy regions where there are less or no cloud in the prior ensemble mean estimates. The performance of ABEI is evaluated through assimilating synthetic and real‐data all‐sky radiance experiments from the Advanced Baseline Imager on board GOES‐16 for Hurricanes Karl of 2010 and Harvey of 2017. Assimilation experiments with ABEI allow adaptive inflation of the ensemble covariance in the model‐simulated clear‐sky regions when there are observed clouds while avoiding unnecessarily large ensemble spread in other cloud scenarios. This new approach alleviates the difficulty in estimating the appropriate inflation factors in the model state space using the innovation statistics in the observation space (radiance) with a highly nonlinear observation operator. It serves as an alternative to existing methods using spatially varying adaptive inflations; their relative performance and potential combinations are to be further assessed in the future.
An adaptive background error inflation (ABEI) method is proposed to calculate a cloud‐scene‐dependent multiplicative covariance inflation factor, which is fully flow‐dependent and spatio‐temporally varying. ABEI is designed to alleviate the difficulty in estimating the inflation factors in the model state space using the innovation statistic in the observation space with a highly nonlinear observation operator. ABEI is found to help initiating convection from the incorrectly estimated clear‐sky conditions, in all‐sky satellite radiance assimilation with an ensemble Kalman filter.
The potential impacts of GOES‐R satellite radiances on tropical cyclone analysis and prediction were examined through ensemble correlations between simulated infrared brightness temperatures and ...various model state variables. The impacts of assimilating GOES‐R all‐sky infrared brightness temperatures on tropical cyclone analysis and prediction were further demonstrated through a series of convection‐permitting observing system simulation experiments using an ensemble Kalman filter under both perfect and imperfect model scenarios. Assimilation of the high temporal and spatial resolution infrared radiances not only constrained well the thermodynamic variables, including temperature, moisture, and hydrometeors, but also considerably reduced analysis and forecast errors in the wind fields. The potential of all‐sky radiances is further demonstrated through an additional proof‐of‐concept experiment assimilating real‐data infrared brightness temperatures from GOES 13 satellite which was operational in an enhanced scanning mode during Hurricane Karl (2010).
Key Points
First study on potential impacts of GOES‐R all‐sky radiances on hurricane analysis and prediction
Examine ensemble correlations of all‐sky radiances with model state variables
Benefits of all‐sky GOES‐R observations are shown for both perfect model and imperfect models
Abstract
Atmospheric deep moist convection has emerged as one of the most challenging topics for numerical weather prediction, due to its chaotic process of development and multiscale physical ...interactions. This study examines the dynamics and predictability of a weakly organized linear convective system using convection permitting EnKF analysis and forecasts with assimilating all-sky satellite radiances from a water vapor sensitive band of the Advanced Baseline Imager on
GOES-16
. The case chosen occurred over the Gulf of Mexico on 11 June 2017 during the NASA Convective Processes Experiment (CPEX) field campaign. Analysis of the water vapor and dynamic ensemble covariance structures revealed that meso-
α
-scale (2000–200 km) and meso-
β
-scale (200–20 km) initial features helped to constrain the general location of convection with a few hours of lead time, contributing to enhancing convective activity, but meso-
γ
-scale (20–2 km) or even-smaller-scale features with less than 30-min lead time were identified to be essential for capturing individual convective storms. The impacts of meso-
α
-scale initial features on the prediction of particular individual convective cells were found to be classified into two regimes; in a relatively dry regime, the meso-
α
-scale environment needs to be moist enough to support the development of the convection of interest, but in a relatively wet regime, a drier meso-
α
-scale environment is preferable to suppress the surrounding convective activity. This study highlights the importance of high-resolution initialization of moisture fields for the prediction of a quasi-linear tropical convective system, as well as demonstrating the accuracy that may be necessary to predict convection exactly when and where it occurs.
Abstract
The dynamics and predictability of the rapid intensification (RI) of Hurricane Harvey (2017) were examined using convection-permitting initialization, analysis, and prediction from a cycling ...ensemble Kalman filter (EnKF) that assimilated all-sky infrared radiances from the Advanced Baseline Imager on
GOES-16
. The EnKF analyses were able to evolve the various scales of the radiance fields associated with Harvey close to those observed, including those associated with scattered individual convective cells before the onset of rapid intensification (RI) and the organized vortex-scale convective system during and after RI. This was true for more than 3 days of a continuous assimilation cycling. Deterministic forecasts initialized from the EnKF analyses captured the rapidly deepening intensity of Harvey more than 24 h prior to its onset. To explore the predictability of Harvey’s intensity during RI, ensemble probabilistic forecasts and sensitivity analyses were conducted. It was found that significant ensemble spread growth was induced by initial perturbations individually in either the wind or moisture fields. The nonlinear interactions between wind and moisture perturbations further limited the predictability of the intensification process of Harvey by increasing the uncertainty in the simulated wind and moisture distributions and modifying the convective activity and its feedback on vortex flow. This study highlights both the importance of better initializing the dynamic and moisture state variables simultaneously and the potential contribution of satellite all-sky radiance assimilation on constraining them and their associated convective activity that impacts RI of tropical cyclones.
Numerous studies have focused on the impact of flooding on road transportation conditions. However, a specific methodology for estimating direct damage by floods on roads considering flood and road ...characteristics is still missing. To bridge this gap, detailed field studies were conducted in rural Bangladesh's Teesta River Basin. The questionnaire was designed to collect information regarding flood characteristics (quantitative flood depth and qualitative flood velocity), road structure (length, width, and height above the ground), structural damage mechanism, and damaged part dimensions (length, width, and thickness) for each road segment. A flood damage function model, which considers the effects of flood factors (depth and velocity) and road characteristics (top surface material, width, and height from the ground), was created using multiple regression analysis. The consistency of this model has been examined by estimating statistical parameters, such as the Akaike information criterion, correlation coefficient, and variance inflation factor. The results showed that earth roads have suffered significant damage in contrast to Bituminous concrete (BC) roads. Less flood damage may result from a road segment that is wider but has a low height from the ground. This study can provide a straightforward model for estimating road damage for flood events in future studies.
•Areliable approach for estimating flood damage to rural roads in developing nationswas proposed.•The velocity and flood depthwereconsidered,askey flood parameters,in the development of the flood damage functions.•The developed flood damage function model also includes the impact of road characteristics.•Provide a straightforward model for estimating road damage for future flood events.
Lack of high‐resolution observations in the inner‐core of tropical cyclones remains a key issue when constructing an accurate initial state of the storm structure. The major implication of an ...improper initial state is the poor predictability of the future state of the storm. The size and associated hazard from strong winds at the inner‐core make it impossible to sample this region entirely. However, targeting regions of the inner‐core where forecasted atmospheric measurements have high uncertainty can significantly improve the accuracy of measurements for the initial state of the storm. This study provides a scheme for targeted high‐resolution observations for small Unmanned Aircraft Systems (sUAS) platforms (e.g., Coyote sUAS) to improve the estimates of the atmospheric measurement in the inner‐core structure. The benefit of observation is calculated based on the high‐fidelity state‐of‐the‐art hurricane ensemble data assimilation system. Potential locations with the most informative measurements are identified through exploration of various simulation‐based solutions depending on the state variables (e.g., pressure, temperature, wind speed, relative humidity) and a combined representation of those variables. A sampling‐based sUAS path planning algorithm considers energy usage when locating the regions of highly uncertain prediction of measurements, allowing sUAS to maximize the benefit of observation. Robustness analysis of our algorithm for multiple scenarios of sUAS drop and goal locations shows satisfactory performance against benchmark similar to current NOAA field campaign. With optimized sUAS observations, a data assimilation analysis shows significant improvements of up to 4% in the tropical cyclone structure estimates after resolving uncertainties at targeted locations.
Key Points
A novel in situ small Unmanned Aircraft Systems (sUAS) observing system for tropical cyclones based on a sampling‐based path planning
Anticipated benefits of in situ sUAS observation scenarios in a high‐fidelity state‐of‐the‐art hurricane ensemble data assimilation system
Optimally sample observations that yield the most informative measurements with lower risk through uncertainty removal under turbulent flow
We examined the orographic effect of Mindanao Island in the Philippines on the distribution of precipitation from Typhoon Washi. The National Centers for Environmental Protection (NCEP) and US ...Department of Energy (DOE) Reanalysis data were downscaled using a regional spectral model to reproduce the typhoon in detail. A heat budget analysis revealed the reason for the absence of precipitation over the mountain in the center of Mindanao Island. Sensitivity experiments with various artificial terrains were conducted to estimate the orographic effect on the precipitation distribution. The peak rainfall, which occurred on the west side of Mt. Ragang, was caused by the interaction between the typhoon and both Mt. Ragang and Mt. Malindang. A rich source of heat and moisture is indispensable for typhoon rainfall. However, without a mountain, a typhoon would not cause heavy precipitation even after trespassing. Divergence of the typhoon wind due to one or more mountains is shown to be the key factor behind the heavy rainfall over land from Typhoon Washi in 2011.
We examined the orographic effect of Mindanao Island in the Philippines on the distribution of precipitation from Typhoon Washi. The National Centers for Environmental Protection (NCEP) and US ...Department of Energy (DOE) Reanalysis data were downscaled using a regional spectral model to reproduce the typhoon in detail. A heat budget analysis revealed the reason for the absence of precipitation over the mountain in the center of Mindanao Island. Sensitivity experiments with various artificial terrains were conducted to estimate the orographic effect on the precipitation distribution. The peak rainfall, which occurred on the west side of Mt. Ragang, was caused by the interaction between the typhoon and both Mt. Ragang and Mt. Malindang. A rich source of heat and moisture is indispensable for typhoon rainfall. However, without a mountain, a typhoon would not cause heavy precipitation even after trespassing. Divergence of the typhoon wind due to one or more mountains is shown to be the key factor behind the heavy rainfall over land from Typhoon Washi in 2011.
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