The present study assesses the added value of high‐resolution maps of precipitable water vapor, computed from synthetic aperture radar interferograms , in short‐range atmospheric predictability. A ...large set of images, in different weather conditions, produced by Sentinel‐1A in a very well monitored region near the Appalachian Mountains, are assimilated by the Weather Research and Forecast (WRF) model. Results covering more than 2 years of operation indicate a consistent improvement of the water vapor predictability up to a range comparable with the transit time of the air mass in the synthetic aperture radar interferograms footprint, an overall improvement in the forecast of different precipitation events, and better representation of the spatial distribution of precipitation. This result highlights the significant potential for increasing short‐range atmospheric predictability from improved high‐resolution precipitable water vapor initial data, which can be obtained from new high‐resolution all‐weather microwave sensors.
Plain Language Summary
Weather forecasts will never be perfect because our models are simplified representations of nature and our observations of the atmosphere are inaccurate. In this study we show, nevertheless, that it is possible to improve such forecasts by interpreting the atmospheric signals in spaceborne radar observations of the Earth surface, indicative of the distribution of water vapor. Better and more detailed maps of water vapor are found to lead to better forecasts not just of water vapor but also of precipitation. A two and a half years assessment covering a wide range of weather conditions in a very well monitored region near the Appalachian Mountains, USA, suggests that the proposed methodology has a significant impact in the quality of the forecasts and could easily be implemented.
Key Points
Assimilation of synthetic aperture radar interferograms consistently extends the skill of hindcasts of water vapor and precipitation
A large fraction of forecast rain errors is due to errors in the distribution of water vapor
The near‐range forecast of heavy precipitation events may be substantially improved with better initial states of water vapor
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Despite a specific data assimilation method, data assimilation (DA) in general can be decomposed into components of the prior information, observation forward operator that is given by the ...observation type, observation error covariances, and background error covariances. In a classic Lorenz model, the influences of the DA components on the initial conditions (ICs) and subsequent forecasts are systematically investigated, which could provide a theoretical basis for the design of DA for different scales of interests. The forecast errors undergo three typical stages: a slow growth stage from 0 h to 5 d, a fast growth stage from 5 d to around 15 d with significantly different error growth rates for ensemble and deterministic forecasts, and a saturation stage after 15 d. Assimilation strategies that provide more accurate ICs can improve the predictability. Cycling assimilation is superior to offline assimilation, and a flow-dependent background error covariance matrix (
P
f
) provides better analyses than a static background error covariance matrix (
B
) for instantaneous observations and frequent time-averaged observations; but the opposite is true for infrequent time-averaged observations, since cycling simulation cannot construct informative priors when the model lacks predictive skills and the flow-dependent
P
f
cannot effectively extract information from low-informative observations as the static
B
. Instantaneous observations contain more information than time-averaged observations, thus the former is preferred, especially for infrequent observing systems. Moreover, ensemble forecasts have advantages over deterministic forecasts, and the advantages are enlarged with less informative observations and lower predictive-skill model priors.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
3.
A View for Atmospheric Unpredictability Cai, Xiuhua; Cao, Hongxing; Fang, Xiaoyi ...
Frontiers in earth science (Lausanne),
07/2021, Volume:
9
Journal Article
Peer reviewed
Open access
Based on Chaotic Dynamics, this paper illustrated the necessity of research and the objective existence of atmospheric unpredictability. Actually, inaccurate forecast happens all the time in both ...operational weather forecasting and climate prediction in which atmospheric unpredictability hides. By means of Discrete Mathematics, this paper also defined the Degree of Hesitation and the Predictable Days with which to discuss and compare the relationship between the predictability and unpredictability of several different forecast objects. In addition, this paper discussed the approaches of evaluating the atmospheric predictability and unpredictability, emphatically showed the Experience Assessment Method. At the last, this paper also proved the existence of atmospheric unpredictability by an example.
The effects of environmental shear on the dynamics and predictability of tropical cyclones (TCs) are further explored through a series of cloud‐permitting ensemble sensitivity experiments with small, ...random initial condition perturbations on the low‐level moisture fields. As an expansion of earlier studies, it is found that larger the shear magnitude, less predictable the TCs, especially the onset time of the rapid intensification (RI), until the shear is too large for the TC formation. Systematic differences amongst the ensemble members begin to arise right after the initial burst of moist convection associated with the incipient vortex. This randomness inherent in moist convection first changes the TC vortex structure subtly, but the location and strength of subsequent moist convection are greatly influential to the precession and alignment of the TC vortex as well as the RI onset time. Additional ensemble sensitivity experiments with different magnitude random perturbations to the mean environmental shear (6 m s−1) show that when the standard deviation of the random shear perturbations among different ensemble members is as small as 0.5 m s−1, the difference in shear magnitude overwhelms the randomness of moist convection in influencing the TC development and rapid intensification (indicative of limited practical predictability). However, for the ensemble with standard deviation of 0.1 m s−1 in random shear perturbations, the uncertainty in TC onset timing is comparable to the ensemble that is perturbed only by small random moisture conditions in the initial moisture field (indicative of the limit in intrinsic predictability).
Key Points:
Predictability of TCs varies under different environmental shear conditions
The larger the shear, the less predictable the TCs until the shear is too large
There are limits in both practical and intrinsic TC predictability
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Abstract
Reservoir computer (RC) is an attractive neural computing framework that can well predict the dynamics of chaotic systems. Previous knowledge of the RC performance is established on the case ...that all variables in a chaotic system are completely observed. However, in practical circumstances the observed variables from a dynamical system are usually incomplete, among which there is a lack of understanding of the RC performance. Here we utilize mean error growth curve to estimate the RC prediction horizon on the Lorenz63 system (L63), and particularly we investigate the scenario of univariate time series. Our results demonstrate that the prediction horizon of RC outperforms that of local dynamical analogs of L63, and the state-space embedding technique can improve the RC prediction in case of incomplete observations. We then test the conclusion on the more complicated systems, and extend the method to estimate the intraseasonal predictability of atmospheric circulation indices. These results could provide indications for future developments and applications of the RC.
A correlation spectrum‐based approach is used to express the theoretical predictability limits of multifractal processes as an analytical function of their anisotropy parameters. This spatially ...anisotropic power law function is then used to investigate the general impact of anisotropy on the predictability of atmospheric fields in the weather regime. The investigation reveals that (i) vertical stratification of a field increases and decreases its super and subsphero‐scale predictability limits, respectively; (ii) trivial horizontal anisotropy slightly improves predictability at all scales; and (iii) horizontal anisotropy together with vertical stratification significantly enhances its predictability over almost the entire scale range. Applying these general results to the case of horizontal wind fields suggests that the interplay between spatial‐anisotropy and atmospheric predictability could account for improvements in forecast skill, commonly observed during the occurrence of rotating thunderstorms and breaks in the Indian summer monsoon.
Plain Language Summary
Quantifying theoretical atmospheric predictability limits is necessary to understand the possibility of making reliable weather predictions. Since atmospheric fields are multifractal and frequently anisotropic with roundish structures near the sphero‐scale, this study expresses the predictability limits via their multifractal and anisotropy parameters for theoretically investigating how spatial anisotropy of a filed impacts its predictability. The investigation shows that horizontal anisotropy moderately increases predictability at all scales, whereas vertical stratification diminishes predictability at scales roughly smaller than the sphero‐scale while enhancing it at larger scales; horizontal anisotropy with vertical stratification, on the other hand, further improves predictability. The spatial anisotropy of horizontal winds seems to be responsible for the extended predictability of organized thunderstorms and monsoon breaks.
Key Points
The theoretical predictability limits of atmospheric fields follow spatially anisotropic scaling laws
Vertical stratification of these fields increases their supersphero‐scale predictability, while trivial horizontal anisotropy slightly improves predictability at all scales
Spatial anisotropy of horizontal wind fields seems to play a crucial role in the extended predictability of supercell thunderstorms and monsoon breaks
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The evaluation of the quality and usefulness of climate modeling systems is dependent upon an assessment of both the limited predictability of the climate system and the uncertainties stemming from ...model formulation. In this study a methodology is presented that is suited to assess the performance of a regional climate model (RCM), based on its ability to represent the natural interannual variability on monthly and seasonal timescales. The methodology involves carrying out multiyear ensemble simulations (to assess the predictability bounds within which the model can be evaluated against observations) and multiyear sensitivity experiments using different model formulations (to assess the model uncertainty). As an example application, experiments driven by assimilated lateral boundary conditions and sea surface temperatures from the ECMWF Reanalysis Project (ERA‐15, 1979–1993) were conducted. While the ensemble experiment demonstrates that the predictability of the regional climate varies strongly between different seasons and regions, being weakest during the summer and over continental regions, important sensitivities of the modeling system to parameterization choices are uncovered. In particular, compensating mechanisms related to the long‐term representation of the water cycle are revealed, in which summer dry and hot conditions at the surface, resulting from insufficient evaporation, can persist despite insufficient net solar radiation (a result of unrealistic cloud‐radiative feedbacks).
A set of global atmospheric simulations has been performed with the ARPEGE-Climat model in order to quantify the contribution of realistic snow conditions to seasonal atmospheric predictability in ...addition to that of a perfect sea surface temperature (SST) forcing. The focus is on the springtime boreal hemisphere where the combination of a significant snow cover variability and an increasing solar radiation favour the potential snow influence on the surface energy budget. The study covers the whole 1950–2000 period through the use of an original snow mass reanalysis based on an off-line land surface model and possibly constrained by satellite snow cover observations. Two ensembles of 10-member AMIP-type experiments have been first performed with relaxed versus free snow boundary conditions. The nudging towards the monthly snow mass reanalysis significantly improves both potential and actual predictability of springtime surface air temperature over Central Europe and North America. Yet, the impact is confined to the lower troposphere and there is no clear improvement in the predictability of the large-scale atmospheric circulation. Further constraining the prescribed snow boundary conditions with satellite observations does not change much the results. Finally, using the snow reanalysis only for initializing the model on March 1st also leads to a positive impact on predicted low-level temperatures but with a weaker amplitude and persistence. A conditional skill approach as well as some selected case studies provide some guidelines for interpreting these results and suggest that an underestimated snow cover variability and a misrepresentation of ENSO teleconnections may hamper the benefit of an improved snow initialization in the ARPEGE-Climat model.
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