Minimum Sea Level Pressure Research Articles

Development of Polar Lows in Future Climate Scenarios over the Barents Sea

Abstract Polar lows (PLs) are intense mesoscale cyclones that form over polar oceans during colder months. Characterized by high wind speeds and heavy precipitation, they profoundly impact coastal communities, shipping, and offshore activities. Amid the substantial environmental changes in polar regions due to global warming, PLs are expected to undergo noteworthy transformations. In this study, we investigate the response of PL development in the Barents Sea to climate warming based on two representative PLs. Sensitivity experiments were conducted including the PLs in the present climate and the PLs in a pseudo–global warming scenario projected by the late twenty-first century for Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP 3-7.0 scenarios from phase 6 of the Coupled Model Intercomparison Project (CMIP6). In both warming climate scenarios, there is an anticipated decrease in PL intensity, in terms of the maximum surface wind speed and minimum sea level pressure. Despite the foreseen increase in latent heat release in the future climate, contributing to the enhancement of PL intensity, other primary factors such as decreased baroclinic instability, heightened atmospheric static stability, and reduced overall surface heat fluxes play pivotal roles in the overall decrease in PL intensity in the Barents Sea under warming conditions. The augmentation of surface latent heat flux, however, results in increased precipitation associated with PLs by enhancing the latent heat release. Furthermore, the regional steering flow shifts in the warming climate can influence the trajectory of PLs during their development. Significance Statement Global warming is anticipated to impact cyclone systems worldwide. Polar lows (PLs), intense mesocyclones in polar regions with potential socioeconomic and human life implications, pose uncertainties regarding intensity changes in a warming climate. In this study, we aimed to better understand how PLs over the Barents Sea will respond to the environmental changes in future climate conditions [Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP 3-7.0] by the end of the twenty-first century. Our results find that the intensity of PLs is expected to decrease in the future while there is an expected increase in precipitation associated with PLs in the warming climate. These findings aim to contribute valuable insights for disaster management strategies in the face of evolving climate scenarios.

Changes in the Typhoon Intensity under a Warming Climate: A Numerical Study of Typhoon Mangkhut

Abstract Accurately assessing cyclone intensity changes due to global warming is crucial for predicting and mitigating sequential hazards. This study develops a high-resolution, fully coupled air–sea model to investigate the impact of global warming on Supertyphoon Mangkhut (2018). A numerical sensitivity analysis is conducted using the pseudo–global warming (PGW) technique based on multiple global climate models (GCMs) from phase 6 of Coupled Model Intercomparison Project (CMIP6). Under ocean warming scenarios, the increasing average sea surface temperature (SST) by 2.26°, 2.44°, 3.45°, and 4.53°C results in reductions in the minimum sea level pressure by 9.2, 10.6, 15.7, and 19.4 hPa, respectively, compared to the original state of Typhoon Mangkhut. Rising SST increases the turbulent heat flux; to be specific, an average SST increase of 2.26°–4.53°C changes the turbulent heat flux into 177%–272% of the original value. Besides, stronger winds enhance SST cooling, including upwelling and entrainment, leading to an increase in the mixed layer depth (MLD). Tropical cyclone heat potential (TCHP) tends to be enhanced under the combined influences as the SST rises. An average increase in the SST of 2.26°, 2.44°, 3.45°, and 4.53°C leads to an increase in the TCHP of 9.94%, 9.85%, 14.67%, and 15.30%, respectively. However, future changes in atmospheric temperature and humidity will moderate typhoon intensification induced by ocean warming. Considering atmospheric conditions, the maximum wind speed decreases by approximately 10% compared to only considering ocean warming. Nevertheless, typhoon intensity is projected to strengthen under future climate change. Significance Statement This study examines the role of global warming in typhoon intensity and the response of typhoon events to changes in the oceanic thermal structure. Sensitivity experiments considering future warming climates are conducted using a fully coupled air–sea numerical model. An average increase in sea surface temperature (SST) by 4.53°C can lead to a reduction in the minimum sea level pressure by 19.4 hPa. Ocean warming enhances oceanic mixing, potentially increasing the availability of heat energy for typhoon’s development (i.e., an average increase in SST by 4.53°C leads to a 15.30% increase in heat energy). However, future changes in atmospheric temperature and humidity will moderate the intensification of typhoons induced by ocean warming. These results are expected to provide information for assessing the future changes in typhoon intensity under a warming climate, which is important for predicting and reducing sequential risks.

A Numerical Study of Tropical Cyclone and Ocean Responses to Air‐Sea Momentum Flux at High Winds

AbstractThe relationship between minimum sea level pressure (MSLP) and maximum wind speed, is a commonly employed metric for assessing tropical cyclone (TC) forecast skill. However, accurately reproducing this relationship in TC forecasts is challenging. By introducing a new air‐sea momentum flux scheme considering both wave state and saturation (decrease) effect at high winds into a fully coupled atmosphere‐ocean‐wave model, our numerical results reveal that the maximum wind speed and MSLP respond oppositely to the air‐sea momentum flux change. The simulated wind‐pressure relationship aligns well with observations when the new flux scheme is used. This study highlights the large sensitivity of wind‐pressure relationship to the air‐sea momentum flux parameterization at high winds. Furthermore, the air‐sea momentum flux has a significant effect on ocean wave characteristics and sea surface temperature (SST) cooling in TC simulations.

A Study on the Possible Brown Ocean Effect: Impacts of an Antecedent Tropical Cyclone on the Rapid Intensification of the 1999 Odisha Supercyclone

AbstractOdhisa, a state of India, bore the disastrous consequences of two consequent Tropical Cyclones (TCs), TC 04B and TC 05B (Odisha 1999 supercyclone), which formed over the Bay of Bengal and experienced landfall in October 1999, with a time gap of fewer than two weeks, over the same region. It is suggested that the first TC, TC 04B, provided an “ocean‐like situation” over the coastal land region, thus delivering the appropriate land conditions that would facilitate the intensification of the following second tropical cyclone, TC 05B; a clear illustration of the “Brown Ocean effect.” Two Weather Research Forecasting (WRF) simulations were conducted, with the control and experimental runs differing solely in the following aspect: the initial cyclonic vortex corresponding to the first TC at the initial time was removed in the experimental run, whereas it was retained in the control run. Both simulations were analyzed to reveal the “Brown Ocean Effect” role. The experimental run result indicates that the minimum central sea level pressure of the second TC was 35 hPa higher than the second TC simulation in the control run. The heavy rainfall associated with TC 04B led to increased soil moisture conditions, providing the second TC (TC 05B) with the necessary conditions for its intensification by the “Brown Ocean Effect.” The results of this study appear to strongly suggest that the “Brown Ocean Effect” could provide one of the main reasons for the extraordinary intensities associated with the 1999 Odisha supercyclone.

Improving the Statistical Representation of Tropical Cyclone In-Storm Sea Surface Temperature Cooling

Abstract This study uses fixed buoy time series to create an algorithm for sea surface temperature (SST) cooling underneath a tropical cyclone (TC) inner core. To build predictive equations, SST cooling is first related to single variable predictors such as the SST before storm arrival, ocean heat content (OHC), mixed layer depth, sea surface salinity and stratification, storm intensity, storm translation speed, and latitude. Of all the single variable predictors, initial SST before storm arrival explains the greatest amount of variance for the change in SST during storm passage. Using a combination of predictors, we created nonlinear predictive equations for SST cooling. In general, the best predictive equations have four predictors and are built with knowledge about the prestorm ocean structure (e.g., OHC), storm intensity (e.g., minimum sea level pressure), initial SST values before storm arrival, and latitude. The best-performing SST cooling equations are broken up into two ocean regimes: when the ocean heat content is less than 60 kJ cm−2 (greater spread in SST cooling values) and when the ocean heat content is greater than 60 kJ cm−2 (SST cooling is always less than 2°C), which demonstrates the importance of the prestorm oceanic thermal structure on the in-storm SST value. The new equations are compared to what is currently used in a statistical–dynamical model. Overall, since the ocean providing the latent heat and sensible heat fluxes necessary for TC intensification, the results highlight the importance for consistently obtaining accurate in-storm upper-oceanic thermal structure for accurate TC intensity forecasts. Significance Statement The ocean provides the heat and moisture necessary for tropical cyclone (TC) intensification. Since the heat and moisture transfer depend on the sea surface temperature (SST), we create statistical equations for the prediction of SST underneath the storm. The variables we use combine the initial SST before the storm arrives, the upper-ocean thermal structure, and the strength and translation speed of the storm. The predictive equations for SST are evaluated for how well they improve TC intensity forecasts. The best-performing equations can be used for prediction in operational statistical models, which can aid intensity forecasts.

Assimilation of new rocket dropsonde data using WRFDA and its impact on numerical simulations of typhoon NORU

On September 26 at 2100 UTC and September 27 at 0900 and 2300 UTC, three rockets platform carrying dropsondes (TFTC-400) devices were launched off the east coast of Hainan Island to conduct a launch experiment aimed at detecting Typhoon NORU (2216). The experiment yielded valuable data that were subsequently analyzed to ascertain temperatures, wind speeds, and relative humidity in the atmosphere. Of the four experiments conducted, employing three distinct control variable configurations (CVs), we utilized the 3DVAR of WRF Data Assimilation (WRFDA) to assimilate rocket sounding data and the NCEP ADP Global Upper Air Observational Weather Data from the research data archive dataset that was jointly produced by the Center for Weather and Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). In one experiment, no data assimilation was performed (CTL). These experiments were designed to assess the impact of these observational datasets on typhoon predictions of the Weather Research & Forecasting Model (WRF) numerical simulations. Utilizing the assimilated background field, a 24-hour forecast was conducted, and the assimilation simulation was analyzed with regard to typhoon path, intensity, precipitation, and improvements in the background field. The results reveal that, on average, the three-assimilation experiment led to a 30 % reduction in track error compared to the CTL. Additionally, the assimilation experiment for CV7 of control variable configurations brought the maximum wind speed closer to observed data than the CTL between 6 and 12 h. The TS (threat score) evaluation of simulated 24-hour precipitation in the model domain indicates that the three assimilation schemes exhibit a degree of improvement in the forecast scores for 24-hour cumulative typhoon precipitation. Nevertheless, the simulation results for minimum sea-level pressure are unsatisfactory. To establish statistical significance, additional cases within the relevant region are necessary for result validation.

Bias Correction of Tropical Cyclone Intensity for Ensemble Forecasts Using the XGBoost Method

Abstract In this study, the extreme gradient boosting (XGBoost) algorithm is used to correct tropical cyclone (TC) intensity in ensemble forecast data from the Typhoon Ensemble Data Assimilation and Prediction System (TEDAPS) at the Shanghai Typhoon Institute (STI), China Meteorological Administration (CMA). Results show that the forecast accuracy of TC intensity may be improved substantially using the XGBoost algorithm, especially when compared with a simple ensemble average of all members in the ensemble forecast [as depicted by the ensemble average (EnsAve) algorithm in this study]. The forecast errors for maximum wind speed (MWS) and minimum sea level pressure (MSLP) have been reduced by a significant margin, ranging from 6.3% to 18.4% for MWS and from 4% to 14.9% for MSLP, respectively. The performance of the XGBoost algorithm is overall better than that of the EnsAve algorithm, although there are a few samples when it is worse. The bias analysis shows that TEDAPS underpredicts the MWS and overpredicts the MSLP, meaning that the TEDAPS underestimates TC intensity. However, the XGBoost algorithm can reduce the bias to improve the forecast accuracy of TC intensity. Specifically, it achieves a reduction of over 20% in forecast errors for both the MWS and MSLP of typhoons compared to the EnsAve algorithm, indicating the XGBoost algorithm’s particular advantage in forecasting intense TCs. These results indicate that the TC intensity forecast can be substantially improved using the XGBoost algorithm, relative to the EnsAve algorithm.

Revisiting Koba’s Relationship to Improve Minimum Sea-Level Pressure Estimates of Western North Pacific Tropical Cyclones

Revisiting Koba’s Relationship to Improve Minimum Sea-Level Pressure Estimates of Western North Pacific Tropical Cyclones

The Effect of Coastline Concavity on Maximum Storm Surge Height along the US Gulf Coast

Storm surge is the most dangerous component of landfalling tropical cyclones (TCs). The growing coastal population highlights the importance of research regarding the atmospheric and geographic factors influencing the maximum storm surge height (MSSH). To date, few studies have investigated the influence of coastline concavity. Here, we investigate the hypothesis that TCs making landfall on a concave coastline will have a higher MSSH than TCs making landfall on a convex coastline. The Colorado State University extended best track dataset includes the radius of 34 kt winds (R34), landfall minimum mean sea level pressure (MSLP), landfall maximum sustained winds, and forward speed of TCs. The storm surge database for the US Gulf Coast provides the location and MSSH for TCs impacting the U.S. Gulf Coast. From this, eleven TCs that meet specific criteria and represent the larger population of Atlantic TCs are selected. The adjusted degree of coastline concavity (ADoC) is calculated for each TC using the law of cosines and 50, 100, and 200 km radius buffers around the point of MSSH. A Mann Whitney U test does not indicate any significant differences between the mean MSSH of TCs making landfall on each coastline type. Additionally, results from a simple linear regression F-test suggest that none of the included parameters have a significant influence on MSSH despite the findings of previous research. Still, the Spearman’s Rho correlation values suggest a weak positive relationship between the ADoC and MSSH. This relationship is significant at the 100 and 200 km buffers, which is consistent with the hypothesis. Results are limited by the small sample size. Future research should use a larger dataset and investigate how each individual storm characteristic affects MSSH. KEYWORDS: Tropical Cyclones; Hurricanes; Storm Surge; Coastal Geography; Coastline Concavity; Gulf of Mexico; Law of Cosines

Microphysics affect the sensitivities of rainfall to different horizontal-resolution simulations: Evidence from a case study of the Weather Research and Forecasting model runs

The Weather Research and Forecasting (WRF) model was used to examine the sensitivities of Typhoon Fitow (2013) to the variations in horizontal mesh spacing ranging from 9 km to 1 km and to different microphysics schemes. The minimum sea level pressure decreased by 5 hPa and the maximum wind speeds increased by 20 m s−1 near the typhoon center as the horizontal grid spacing decreased from 9 km, 3 km, and 1 km in both Purdue-Lin and National Severe Storms Laboratory (NSSL) microphysics. The strengthening of the tropical cyclone may have been due to similar physical processes in the Purdue-Lin and NSSL simulations. Fine-resolution simulations produced localized and intense rainfall, in correspondence to localized and intense upward motions. Moreover, as the horizontal mesh spacing reduced from 9 km to 1 km, the distributions of the upward and downward motions broadened and the radar reflectivity bins with contour frequencies >10% increased. The simulated rainfall in the NSSL with a horizontal grid spacing below 3 km and in Purdue-Lin with a 1 km grid spacing reproduced spatial and temporal distributions similar to the actual observations. This suggested that the elaborate microphysics may compensate for the lack of horizontal resolution, to some extent. The precipitation budget analysis further suggested that the diminishment of rainfall was attributed to the reduced net condensation and hydrometeor convergence respectively in the Purdue-Lin and NSSL scheme as the mesh spacing reduced, which was further attributable to the decreased condensation or deposition.

The effects of boundary layer vertical turbulent diffusivity on the tropical cyclone intensity

The sensitivity of the simulated intensification of Typhoon Mangkhut (2018) to boundary layer turbulent diffusivity (Km) is investigated through a series of numerical simulations using the modified Km from the Yonsei-University (YSU) planetary boundary layer (PBL) scheme in the Weather Research and Forecasting (WRF) model. Two intensity metrics, namely grid-point and area-averaged metrics, were employed to evaluate the simulations. The results indicate that a smaller Km tends to result in a smaller grid-point minimum central sea level pressure (MSLP), while producing a larger area-averaged MSLP and smaller area-averaged maximum 10 m wind speed (VMAX), which is consistent with a weaker radial inflow averaged over the area of a 500 km radius. Furthermore, the surface latent heat flux exhibits a nearly linear relationship with Km throughout the simulation. Overall, a larger Km tends to produce a greater diabatic heating rate, although this relationship is not as clear as that between Km and latent heat flux due to the presence of spontaneous asymmetric convective eddy features. Moreover, since a larger Km tends to result in a larger 10 m tangential wind, the size of a tropical cyclone (TC) defined by the radius at which 10 m tangential wind speed becomes 10 m s−1 shows a positive linear relationship with Km.

IDENTIFICATION OF POTENTIAL STORM SURGES DUE TO TYPHOON RAI USING NUMERICAL MODELS

In December 2021, Typhoon Rai (local name: Odette) hit the Visayas region of the Philippines. It had a minimum sea level pressure (MSLP) of 915 hectopascals (hPa) and a maximum sustained wind speed of 54.02 m/s and was classified as a violent typhoon by the Japan Meteorological Agency. Rai caused massive damage to infrastructure and housing (NDRRMC 2022). Rai passed through the vulnerable islands of Bohol and Cebu, where high storm surge heights could be expected from strong typhoons (Lapidez et al., 2015). Field surveys were conducted right after a typhoon event to investigate the condition of the affected areas. However, Rai hit during the pandemic when travel restrictions were effective in the country. Thus, it was difficult to identify the areas hit by potential storm surges immediately. Satellite imagery is one method; however, it is generally difficult to identify if the area was affected by storm surges using images. Numerical modeling of the typhoon event over select areas is one way to solve the problem of disaster information shortage. In this study, Rai is simulated using weather and storm models over the provinces of Bohol and Cebu.

Large-eddy simulation of the rapidly intensifying tropical cyclone Soudelor (2015)

The lack of dense observations of tropical cyclones (TCs) far out at sea hinders mankind's understanding of their multiscale physics. Large-eddy simulation (LES) provides a potential surrogate considering its ability to reproduce the fine structures of a wide range of atmospheric flows including TC. In this study, an LES is carried out to reproduce the rapid intensification of TC Soudelor (2015). Comparisons with both observations and previous modeling studies indicate that the LES produces a reasonably accurate depiction of the TC structure, including the active rainbands, the remarkable secondary circulation and typical double warm cores. Notably, the LES captures exclusive features of the TC, such as the tornado-scale vortices (TSVs) reported in previous LES studies at much finer horizontal resolution. Local gust winds in these vortices are related to the unexpected minimum sea-level pressure, which may affect the determination of the TC intensity. To address this issue, a tractable correction algorithm is developed based on the systematic analysis of TSVs. This algorithm aims to help researchers better interpret the booming LESs of TC both at present and in the future.

Role of the Thermodynamic Structure of the Inner Core in Predicting the Intensification of Hurricane Patricia (2015)

AbstractWe use short‐range ensemble forecasts and ensemble clustering analysis to study the factors affecting the intensification of Hurricane Patricia (2015). Convection‐permitting ensemble forecasts are classified into two groups: 10 spin‐down (SPD) members and 10 spin‐up (SPU) members with intensification rates of <0 and >0 m s−1 for the first 6 hr, respectively. Ensemble clustering analysis found that the wind–pressure relationship was incorrect in the SPD group, indicating that the SPD issue may be partly caused by an imbalance in the initial minimum sea‐level pressure (MSLP) and the maximum wind speed (MWS). The SPD issue appears to be related to three main points: (a) a weaker upper‐level warm core; (b) a drier inner core at low levels; and (c) a dry, cold air intrusion at mid‐levels. In contrast, the SPU group has a stronger upper‐level warm core and a relatively wet inner core at lower levels, as well as a relatively strong secondary circulation. These favorable initial conditions in the SPU group, combined with a greater updraft and stronger convection around the eyewall, result in more latent heating around or in the eyewall that favors the intensification of the tropical cyclone. Comparisons between the SPD and SPU groups suggest that the analyzed ensemble could not accurately capture the relationship between the initial MSLP and MWS, which, combined with the unfavorable thermodynamic conditions at the initial time, resulted in the incorrect evolution of the intensity. Therefore, improving the initial conditions appears to be an effective way to address the SPD issue.

Strong surface winds in Storm Eunice. Part 1: storm overview and indications of sting jet activity from observations and model data

Strong surface winds in Storm <i>Eunice</i>. Part 1: storm overview and indications of sting jet activity from observations and model data

Assessing the performance of the HARMONIE-AROME and WRF-ARW numerical models in North Atlantic Tropical Transitions

Tropical cyclones (TCs) can develop as a result of the tropical transition (TT) process, which occurs when an extratropical cyclone (EC) begins to exhibit tropical characteristics, forming a TC. In this study, four TT processes that lead to a hurricane structure [Delta (2005), Ophelia (2017), Leslie (2018), and Theta (2020)] are evaluated using two high-resolution numerical models (WRF and HARMONIE-AROME). Both tracks and intensities of the cyclones are assessed by comparing the simulated minimum sea level pressure and maximum wind speed to an observational dataset. Moreover, a spatial verification is performed by comparing the MSG-SEVIRI brightness temperature (BT) and accumulated precipitation (IMERG) to the corresponding simulations accomplished by both models. Analyzing the track results, the WRF model, on average, outstands HARMONIE-AROME. However, it is the HARMONIE-AROME model that performs better than WRF when reproducing the intensity of these cyclones. Concerning the BT spatial validation, HARMONIE-AROME slightly outperformed WRF when reproducing the cyclone's structure but failed when simulating the BT amplitude. Besides, both models achieved a nearly perfect cyclone location. In terms of accumulated precipitation results, the HARMONIE-AROME model overestimates the larger structures while underestimating the smaller ones, whereas the WRF model underestimates the bigger structures, being poorly located by both models. Although it is difficult to establish which numerical model performs better, the overall results show an outstanding of the HARMONIE-AROME model over the WRF model when simulating TT processes.

Transformer-based tropical cyclone track and intensity forecasting

Tropical cyclone (TC) is one of the most destructive natural disasters, and hence it is very crucial to predict attributes of TC accurately. In this paper, a transformer-based method for short-term prediction of the track and intensity of TCs, including the central latitude, the central longitude, the minimum sea level pressure and the maximum sustained wind speed, in the Northwest Pacific Basin is proposed. The data set used in this study ranging from 1980 to 2021 was released by China Meteorological Administration (CMA). A data preprocessing step including feature augmentation is adopted, which can make full use of the historical information of TCs. The transformer networks can model the complex dynamic relationships between the time series data and dig the relationship between different attributes based on the multi-head self-attention mechanism. The transformer-based method proposed in this study exhibits excellent performance in the prediction task. Meanwhile, compared to the traditional recurrent neural network models including gated recurrent units model and long short-term memory model, the transformer model has a better performance in predicting all attributes. This method provides a practical guidance of using advanced AI-based approaches to predict TC attributes.

Prediction of Extremely Severe Cyclonic Storm “Fani” Using Moving Nested Domain

The prediction of extremely severe cyclonic storms has been a long-standing and challenging issue due to their short life period and large variation in intensities over a short time. In this study, we predict the track, intensity, and structure of an extremely severe cyclonic storm (ESCS) named ‘Fani,’ which developed over the Bay of Bengal region from 27 April to 4 May 2019, using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model. Two numerical experiments were conducted using the moving nested domain method with a 3 km horizontal resolution, one with the FLUX-1 air-sea flux parameterization scheme and the other with the FLUX-2 air-sea flux parameterization scheme. The NCEP operational Global Forecast System (GFS) analysis and forecast datasets with a 25 km horizontal resolution were used to derive the initial and boundary conditions. The WRF model’s predicted track and intensity were validated with the best-fit track dataset from the India Meteorological Department (IMD), and the structure was validated with different observations. The results showed that the WRF model with the FLUX-1 air-sea parameterization scheme accurately predicted the track, landfall (position and time), and intensity (minimum sea level pressure and maximum surface wind) of the storm. The track errors on days 1 to 4 were approximately 47 km, 123 km, 96 km, and 27 km in the FLUX-1 experiment and approximately 54 km, 142 km, 152 km, and 166 km in the FLUX-2 experiment, respectively. The intensity was better predicted in the FLUX-1 experiment during the first 60 h, while it was better predicted in the FLUX-2 experiment for the remaining period. The structure, in terms of relative humidity, water vapor, maximum reflectivity, and temperature anomaly of the storm, was also discussed and compared with available satellite and Doppler Weather Radar observations.

A Study of Simultaneous Assimilation of Coastal Ground‐Based and Airborne Radar Observations on the Prediction of Harvey (2017) With the Hourly 3DEnVar System for HWRF

AbstractThis study investigates the relative impact of assimilating the ground‐based WSR‐88D radar (GBR) and the airborne Tail Doppler Radar (TDR) observations on the analysis and prediction of Hurricane Harvey (2017) during its landfalling stage. Results show that the assimilation of GBR (experiment “DAG”) outperforms the assimilation of TDR (experiment “DAT”) in multiple aspects. For example, “DAG” produces better predictions in Vmax, radial velocity, brightness temperature, and precipitation than “DAT.” The advantages of “DAG” over “DAT” are likely from the better analyzed thermodynamical structures in addition to the better data availability. The average Minimum Sea Level Pressure (MSLP) prediction error is the only aspect of “DAG” that is inferior to “DAT.” Diagnostics show that such an inferior performance of MSLP for “DAG” is associated with the systematic bias from the Hurricane Weather Research and Forecasting model. The combined assimilation of both observations (experiment “DAB”) shows complementary effects and performs the best overall among all experiments.

QuickOSSE Research on the Impact of Airship-Borne Doppler Radar Radial Winds to Predict the Track and Intensity of a Tropical Cyclone

Different from the aircraft TC reconnaissance flight missions before, a tropical cyclone (TC) field campaign project with a Doppler radar equipped on an airship that could hang over on the top of a TC (about 20 km) has been recently carried out in China. To understand the impact of airship-borne radar radial wind observations in TC forecasting, this work conducted quick observation simulation system experiments (QuickOSSE) by assimilating simulated airship-borne Doppler radar radial winds with an Ensemble Kalman Filter (EnKF) algorithm. The results show that airship-borne radial winds assimilation reproduces the forecasted track and minimum sea level pressure of the nature run. The forecast of dynamic and thermodynamic TC structures, such as tangential wind, secondary circulation, and warm core, are also improved. In addition, two determining factors, the radar depression angle (D-ang) and the distance from the airship to the TC center (DIS), are found to primarily affect the forecast of the TC track and intensity, respectively. Benefiting from a larger horizontal coverage, observations under a smaller D-ang improved the track more significantly. Meanwhile, the intensity forecast error with a DIS around the radius of the maximum wind is the smallest among several sensitive experiments, which may because the peak-velocity winds representing the TC’s intensity could be observed by radar. The results are expected to help establish an observational strategy for upcoming airship flight missions in practice.