Abstract
The assimilation of satellite all-sky infrared (IR) brightness temperatures (BTs) has been shown in previous studies to improve intensity forecasts of tropical cyclones. In this study, we ...examine whether assimilating all-sky IR BTs can also potentially improve tropical cyclogenesis forecasts by improving the pregenesis cloud and moisture fields. By using an ensemble-based data assimilation system, we show that the assimilation of upper-tropospheric water vapor channel BTs observed by the
Meteosat-10
SEVIRI instrument two days before the formation of a tropical depression improves the genesis forecast of Hurricane Irma (2017), a classic Cape Verde storm, by up to 24 h while also capturing its later rapid intensification in deterministic forecasts. In an experiment that withholds the assimilation of all-sky IR BTs, the assimilation of conventional observations from the Global Telecommunications System (GTS) leads to the premature genesis of Hurricane Irma by at least 24 h. This premature genesis is shown to result from an overestimation of the spatial coverage of deep convection within the African easterly wave (AEW) from which Irma eventually forms. The gross overestimation of deep convection without all-sky IR BTs is accompanied by higher column saturation fraction, stronger low-level convergence, and the earlier spinup of a low-level meso-
β
-scale vortex within the AEW that ultimately becomes Hurricane Irma. Through its adjustment to the initial moisture and cloud conditions, the assimilation of all-sky IR BTs leads to a more realistic convective evolution in forecasts and ultimately a more realistic timing of genesis.
Significance Statement
Every year hurricanes impact the lives of thousands of people living along the eastern coast of the United States. Many of these storms originate from tropical disturbances that exit the west coast of Africa. To give the public more warning time ahead of these storms, it is important to improve the forecasts of their formation. This study uses a system developed at The Pennsylvania State University to incorporate satellite observations into forecasts of a classic Cape Verde storm, Hurricane Irma (2017), two days before it formed. By using satellite-collected radiances, we improve the timing of its formation by up to 24 h due to a better representation of the mesoscale tropical disturbance from which it originated.
The advent of modern geostationary satellite infrared radiance observations has noticeably improved numerical weather forecasts and analyses. However, compared to midlatitude weather systems and ...tropical cyclones, research into using infrared radiance observations for numerically predicting and analyzing tropical mesoscale convective systems remain mostly fallow. Since tropical mesoscale convective systems play a crucial role in regional and global weather, this deficit should be addressed. This study is the first of its kind to examine the potential impacts of assimilating all-sky upper tropospheric infrared radiance observations on the prediction of a tropical squall line. Even though these all-sky infrared radiance observations are not directly affected by lower-tropospheric winds, the high-frequency assimilation of these all-sky infrared radiance observations improved the analyses of the tropical squall line’s outflow position. Aside from that, the assimilation of all-sky infrared radiance observations improved the analyses and prediction of the squall line’s cloud field. Finally, reducing the frequency of assimilating these all-sky infrared radiance observations weakened these improvements to the analyzed outflow position, as well as the analyses and predictions of cloud fields.
The meteorological characteristics of cloudy atmospheric columns can be very different from their clear counterparts. Thus, when a forecast ensemble is uncertain about the presence/absence of clouds ...at a specific atmospheric column (i.e., some members are clear while others are cloudy), that column's ensemble statistics will contain a mixture of clear and cloudy statistics. Such mixtures are inconsistent with the ensemble data assimilation algorithms currently used in numerical weather prediction. Hence, ensemble data assimilation algorithms that can handle such mixtures can potentially outperform currently used algorithms. In this study, we demonstrate the potential benefits of addressing such mixtures through a bi‐Gaussian extension of the ensemble Kalman filter (BGEnKF). The BGEnKF is compared against the commonly used ensemble Kalman filter (EnKF) using perfect model observing system simulated experiments (OSSEs) with a realistic weather model (the Weather Research and Forecast model). Synthetic all‐sky infrared radiance observations are assimilated in this study. In these OSSEs, the BGEnKF outperforms the EnKF in terms of the horizontal wind components, temperature, specific humidity, and simulated upper tropospheric water vapor channel infrared brightness temperatures. This study is one of the first to demonstrate the potential of a Gaussian mixture model EnKF with a realistic weather model. Our results thus motivate future research toward improving numerical Earth system predictions though explicitly handling mixture statistics.
Plain Language Summary
The accuracy of a computer weather forecast often depends on the accuracy of the information inputted into the computer forecast system. The accuracy of the input in turn depends on the accuracy of the input‐constructing algorithm. Such algorithms often use probabilistic forecasts from an earlier point in time and current atmospheric measurements to construct the inputs. A common assumption in such algorithms is that the probabilistic forecasts follow a multivariate normal distribution (henceforth called the normality assumption). However, in the frequent situation where the probabilistic forecast is uncertain about the presence/absence of clouds, the normality assumption is violated. This is because clear atmospheric columns and cloudy atmospheric columns have distinctly different thermodynamic and dynamic characteristics. These two types of columns thus have different statistics. As such, when a probabilistic forecast is uncertain about the presence/absence of clouds, it has mixed statistics (henceforth termed mixed probabilistic forecast). Addressing such mixtures can potentially improve forecasts. In this study, we propose a new input‐constructing algorithm that can explicitly handle mixed probabilistic forecasts. This new algorithm is nearly as fast as currently available algorithms. More importantly, our experiments demonstrate that our new algorithm can produce more accurate forecast inputs than an existing popular algorithm. Our work thus suggests that weather forecasts can be improved by upgrading input‐constructing algorithms to treat a common situation where the normality assumption is violated.
Key Points
Current ensemble data assimilation (DA) methods assume that forecasts follow a normal distribution. This assumption is often invalid
In this study, we propose a computationally efficient ensemble DA method that handles clear and cloudy forecasts separately
This study uses a realistic weather model (WRF) to show that this method can outperform the EnKF
Abstract
The introduction of infrared water vapor channel radiance ensemble data assimilation (DA) has improved numerical weather forecasting at operational centers. Further improvements might be ...possible through extending ensemble data assimilation methods to better assimilate infrared satellite radiances. Here, we will illustrate that ensemble statistics under clear-sky conditions are different from cloudy conditions. This difference suggests that extending the ensemble Kalman filter (EnKF) to handle bi-Gaussian prior distributions may yield better results than the standard EnKF. In this study, we propose a computationally efficient bi-Gaussian ensemble Kalman filter (BGEnKF) to handle bi-Gaussian prior distributions. As a proof-of-concept, we used the 40-variable Lorenz 1996 model as a proxy to examine the impacts of assimilating infrared radiances with the BGEnKF and EnKF. A nonlinear observation operator that constructs radiance-like bimodal ensemble statistics was used to generate and assimilate pseudoradiances. Inflation was required for both methods to effectively assimilate pseudoradiances. In both 800- and 20-member experiments, the BGEnKF generally outperformed the EnKF. The relative performance of the BGEnKF with respect to the EnKF improved when the observation spacing and time between DA cycles (cycling interval) are increased from small values. The relative performance then degraded when observation spacing and cycling interval become sufficiently large. The BGEnKF generated less noise than the EnKF, suggesting that the BGEnKF produces more balanced analysis states than the EnKF. This proof-of-concept study motivates future investigation into using the BGEnKF to assimilate infrared observations into high-order numerical weather models.
Abstract
Geostationary infrared satellite observations are spatially dense >1/(20 km)
2
and temporally frequent (>1 h
−1
). These suggest the possibility of using these observations to constrain ...subsynoptic features over data-sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary
Meteorological Satellite 7
(
Meteosat-7
) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF). Comparisons against the independently observed
Meteosat-7
window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than the EnSRF algorithm.
Modern global reanalysis products have greatly accelerated meteorological research in synoptic‐to‐planetary‐scale phenomena. However, their use in studying tropical mesoscale convective systems ...(MCSs) and their regional‐to‐global impact has mostly been limited to supplying initial and boundary conditions for MCS‐resolving simulations and providing information about the large‐scale environments of MCSs. These limitations are due to difficulties in resolving tropical MCS dynamics in the relatively low‐resolution global models and that tropical MCSs often occur over poorly observed regions. In this work, a Tropical MCS‐resolving Reanalysis product (TMeCSR) was created over a region with frequent tropical MCSs. This region spans the tropical Indian Ocean, tropical continental Asia, Maritime Continent, and Western Pacific. TMeCSR is produced by assimilating all‐sky infrared radiances from geostationary satellites and other conventional observations into an MCS‐resolving regional model using the Ensemble Kalman Filter. The resulting observation‐constrained high‐resolution (9‐km grid spacing) data set is available hourly during the boreal summer (June‐August) of 2017, during which widespread severe flooding occurred. Comparisons of TMeCSR and European Center for Medium Range Weather Forecast Reanalysis version 5 (ERA5) against independent satellite retrievals indicate that TMeCSR's cloud and multiscale rain fields are better than those of ERA5. Furthermore, TMeCSR better captured the diurnal variability of rainfall and the statistical characteristics of MCSs. Forecasts initialized from TMeCSR also have more accurate rain and clouds than those initialized from ERA5. The TMeCSR and ERA5 forecasts have similar performances with respect to sounding and surface observations. These results indicate that TMeCSR is a promising MCS‐resolving data set for tropical MCS studies.
Plain Language Summary
Thunderstorms provide much of the rainfall over the Tropics and have important impacts on global weather and climate. However, these important systems often occur over regions with sparse in‐situ observations. Hence, it is difficult to use in‐situ observations to study the detailed dynamics and thermodynamics of these thunderstorm systems. While combining observations with computer simulation data can produce three‐dimensional data sets over the Tropics, the currently available combination data sets have difficulty resolving these thunderstorm systems. In this study, we combined high‐resolution satellite measurements with high‐resolution weather simulations to produce a high‐resolution four‐dimensional data set. This new data set can capture tropical thunderstorm systems over an area spanning the tropical Indian Ocean to the western edge of Pacific Ocean. We compared the accuracy of our new data set against a gold standard global data set. Using independent satellite‐derived radiation and rainfall data, we found that our new data set has more accurate storm characteristics compared to the gold standard. These characteristics include clouds and rainfall. Furthermore, simulations initialized from our new data set had a similar advantage over simulations initialized from the gold standard. These promising results suggest that our new data set might be better at capturing tropical thunderstorm systems than the gold standard.
Key Points
Tropical mesoscale convective systems (MCSs) research can benefit from an observation‐constrained MCS‐resolving reanalysis data set
We produced such a data set using all‐sky satellite infrared radiances, MCS‐resolving regional simulations, and ensemble data assimilation
Compared to European Center for Medium Range Weather Forecast Reanalysis version 5, the new data set better captured cloud, rainfall, and frequency of tropical MCSs and produced better short‐term forecasts
Ensemble‐based data assimilation of radar observations across inner‐core regions of tropical cyclones (TCs) in tandem with satellite all‐sky infrared (IR) radiances across the TC domain improves TC ...track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all‐sky microwave (MW) radiances using Hurricane Harvey (2017) as an example. Assimilating Global Precipitation Measurement constellation all‐sky MW radiances in addition to GOES‐16 all‐sky IR radiances reduces the forecast errors in the TC track, rapid intensification (RI), and peak intensity compared to assimilating all‐sky IR radiances alone, including a 24‐hr increase in forecast lead‐time for RI. Assimilating all‐sky MW radiances also improves Harvey's hydrometeor fields, which leads to improved forecasts of rainfall after Harvey's landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC‐associated hazards in the future.
Plain Language Summary
Track, intensity, and rainfall are fundamental elements of all forecasts and warnings associated with tropical cyclones (TCs). Over the last few decades, the forecast community has significantly improved TC track forecasts. Notable improvements in TC intensity forecasts have recently been achieved using high‐resolution models and remote‐sensing observations over the inner‐core region of TCs. This study builds on these earlier efforts by investigating the impacts of microwave (MW) observations on the forecast accuracy of TC track, intensity, and rainfall. Because MW radiances are sensitive to water vapor, liquid water, and ice, assimilating these observations into numerical TC forecasts is expected to improve estimates of the liquid water and ice within TCs, leading to better rainfall forecasts. These expectations are borne out in our study of Hurricane Harvey. Results indicate that incorporating currently available yet underutilized observations into numerical TC forecasts can further improve warnings of, and preparedness for, TC‐associated hazards in the future.
Key Points
Satellite all‐sky infrared (IR) and microwave (MW) radiances are assimilated to assess their impacts on forecasts for Hurricane Harvey
Along with IR radiances, MW radiances improve the track and intensity forecasts for Harvey
MW radiance assimilation leads to better analyses of the hydrometeor fields and more accurate rainfall forecasts
The existence of outliers can seriously influence the analysis of variational data assimilation. Quality control allows us to effectively eliminate or absorb these outliers to produce better analysis ...fields. In particular, variational quality control (VarQC) can process gray zone outliers and is thus broadly used in variational data assimilation systems. In this study, governing equations are derived for two VarQC algorithms that utilize different contaminated Gaussian distributions (CGDs): Gaussian plus flat distribution and Huber norm distribution. As such, these VarQC algorithms can handle outliers that have non-Gaussian innovations. Then, these VarQC algorithms are implemented in the Global/Regional Assimilation and PrEdiction System (GRAPES) model-level three-dimensional variational data assimilation (m3DVAR) system. Tests using artificial observations indicate that the VarQC method using the Huber distribution has stronger robustness for including outliers to improve posterior analysis than the VarQC method using the Gaussian plus flat distribution. Furthermore, real observation experiments show that the distribution of observation analysis weights conform well with theory, indicating that the application of VarQC is effective in the GRAPES m3DVAR system. Subsequent case study and long-period data assimilation experiments show that the spatial distribution and amplitude of the observation analysis weights are related to the analysis increments of the mass field (geopotential height and temperature). Compared to the control experiment, VarQC experiments have noticeably better posterior mass fields. Finally, the VarQC method using the Huber distribution is superior to the VarQC method using the Gaussian plus flat distribution, especially at the middle and lower levels.
Small forecast ensemble sizes (< 100) are common in the ensemble data assimilation (EnsDA) component of geophysical forecast systems, thus limiting the error-constraining power of EnsDA. This study ...proposes an efficient and embarrassingly parallel method to generate additional ensemble members: the Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC; “peace gee see”). Such members are called “virtual members”. PESE-GC utilizes the users' knowledge of the marginal distributions of forecast model variables. Virtual members can be generated from any (potentially non-Gaussian) multivariate forecast distribution that has a Gaussian copula. PESE-GC's impact on EnsDA is evaluated using the 40-variable Lorenz 1996 model, several EnsDA algorithms, several observation operators, a range of EnsDA cycling intervals, and a range of forecast ensemble sizes. Significant improvements to EnsDA (p<0.01) are observed when either (1) the forecast ensemble size is small (≤20 members), (2) the user selects marginal distributions that improve the forecast model variable statistics, and/or (3) the rank histogram filter is used with non-parametric priors in high-forecast-spread situations. These results motivate development and testing of PESE-GC for EnsDA with high-order geophysical models.
Abstract
Recent studies have shown that the assimilation of all-sky infrared (IR) observations can be beneficial for tropical cyclone analyses and predictions. The assimilation of Tail Doppler Radar ...(TDR) radial velocity observations has also been shown to improve tropical cyclone analyses and predictions; however, there is a paucity of literature on the impacts of simultaneously assimilating them with all-sky infrared IR brightness temperatures (BTs). This study examines the impacts of assimilating combinations of GOES-16 all-sky IR brightness temperatures, NOAA P-3 TDR radial velocities, and conventional observations from the Global Telecommunications System (GTS) on the analyses and forecasts of Hurricane Dorian (2019). It is shown that including IR and/or TDR observations on top of conventional GTS observations significantly reduces both track and intensity forecast errors. Track errors are reduced the most (25% at lead times greater than 48 h) when TDR and GTS observations are assimilated. In terms of intensity, errors are always lower at lead times greater than 48 h when IR BTs are assimilated. Simultaneously assimilating TDR and IR observations has the potential to further improve the intensity forecast by as much as 37% at a lead time of 48 h to 72 h. The improved intensity forecasts produced by the experiments assimilating all three observation sources are shown to be a result of the competing effects of IR assimilation producing an overly broad area of strong cyclonic circulation and TDR assimilation constraining that circulation to a more realistic size and intensity. Interestingly, the order in which observations are assimilated has non-negligible impacts on the analyses and forecasts of Dorian.
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