Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS

Being able to effectively identify clouds and monitor their evolution is one important step toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation technique is developed using tools from image processing techniques. This method integrates morphological image gradient magnitudes to separable cloud systems and patches boundaries. A varying scale-kernel is implemented to reduce the sensitivity of image segmentation to noise and capture objects with various finenesses of the edges in remote-sensing images. The proposed method is flexible and extendable from single- to multi-spectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellites (GOES-R) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Cloud Classification System) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potential to improve rain detection and estimation skills with an average of more than 45% gain comparing to the segmentation technique used in PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to 98%

On the Role of Surface Fluxes and WISHE in Tropical Cyclone Intensification

The authors show that the feedback between surface wind and surface enthalpy flux is an important influence on tropical cyclone evolution, even though, as with at least some classical instability mechanisms, such a feedback is not strictly necessary. When the wind speed is artificially capped in idealized numerical experiments, storm development is slowed and storms achieve a smaller final intensity. When it is capped in simulations of an actual storm (Hurricane Edouard of 2014), the quality of the simulations is strongly compromised; for example, little development occurs when the wind speed is capped at 5 m s{superscript −1], in contrast to the category-3 hurricane shown by observations and produced by the control experiment.National Science Foundation (U.S.) (Grant AGS 1305798)United States. Office of Naval Research (Grant N000140910526)United States. Office of Naval Research (Grant N000141410062)Massachusetts Institute of Technology (Houghton Lecturer Fund

Dynamics and Predictability of Hurricane Humberto (2007) Revealed from Ensemble Analysis and Forecasting

This study uses short-range ensemble forecasts initialized with an Ensemble-Kalman filter to study the dynamics and predictability of Hurricane Humberto, which made landfall along the Texas coast in 2007. Statistical correlation is used to determine why some ensemble members strengthen the incipient low into a hurricane and others do not. It is found that deep moisture and high convective available potential energy (CAPE) are two of the most important factors for the genesis of Humberto. Variations in CAPE result in as much difference (ensemble spread) in the final hurricane intensity as do variations in deep moisture. CAPE differences here are related to the interaction between the cyclone and a nearby front, which tends to stabilize the lower troposphere in the vicinity of the circulation center. This subsequently weakens convection and slows genesis. Eventually the wind-induced surface heat exchange mechanism and differences in landfall time result in even larger ensemble spread.

Environmental influences on the intensity changes of tropical cyclones over the western North Pacific

The influence of environmental conditions on the intensity changes of tropical cyclones (TCs) over the western North Pacific (WNP) is investigated through examination of 37 TCs during 2000–2011 that interacted directly with the western North Pacific subtropical high (WNPSH). Comprehensive composite analysis of the environmental conditions is performed for two stages of storms: one is categorized as intensifying events (maximum wind speed increases by 15 kn over 48 h) and the other is categorized as weakening events (maximum wind speed decreases by 15 kn over 48 h). Comparison of the composite analysis of these two cases show that environmental conditions associated with the WNPSH play important roles in the intensity changes of TCs over the WNP. When a TC moves along the southern periphery of the WNPSH, the relatively weaker easterly environmental vertical wind shear helps bring warm moist air from the south and southeast to its southeast quadrant within 500 km, which is favorable for the TC to intensify. However, when a TC moves along the western edge of the WNPSH, under the combined influences of the WNPSH and an upper-level westerly trough, a strong westerly vertical shear promotes the intrusion of dry environmental air associated with the WNPSH from the north and northwest, which may lead to the inhibition of moisture supply and convection over the western half of the TC and thus its weakening. These composite results are consistent with those with additional geographic restrictions, suggesting that the dry air intrusion and the vertical wind shear (VWS) associated with the WNPSH, indeed affect the intensity changes of TCs over the WNP beyond the difference related solely to variations in geographical locations. The average sea surface temperature (SST) of 27.6 °C for the weakening events is also lower than an average of 28.9 °C for the strengthening events, but remains above the critical value of 27 °C for TC intensification, suggesting that the SST may be regarded as a less positive factor for the weakening events

Extreme Rainfall in Texas: Patterns and Predictability

Extreme rainfall, with storm total precipitation exceeding 500 mm, occurs several times per decade in Texas. According to a compositing analysis, the large-scale weather patterns associated with extreme rainfall events involve a northward deflection of the tropical trade winds into Texas, with deep southerly winds extending into the middle troposphere. One such event, the July 2002 South-Central Texas flood, is examined in detail. This particular event was associated with a stationary upper-level trough over central Texas and northern Mexico that established a steady influx of tropical moisture from the south. While the onset of the event was triggered by destabilization caused by an upper-level vortex moving over the northeast Mexican coast, a succession of upper-level processes allowed the event to become stationary over south-central Texas and produce heavy rain for several days. While the large-scale signatures of such extreme rain events evolve slowly, the many interacting processes at smaller scales make numerical forecasts highly sensitive to details of the simulations

A Hybrid Pso Algorithm for Order Assignment Problem with Buffer Zone in Holonic Manufacturing System

Abstract. In order to make the enterprise assessment system become more intelligent and efficient for the application and realization technology of advanced manufacturing technology, the enterprise alliance and its business reconfiguration model for Holonic Manufacturing System(HMS) are processed. The enterprise alliance with dynamic reconfiguration and Holon feature is established by constructing the information platform to Holonic Manufacturing system. The system can realize the informationize in enterprise and between enterprise. The cooperation between enterprises can also be supported. The task assignment problem in the enterprise with directed graph model is presented. Task assignment problem with buffer zone is solved via a hybrid PSO algorithm. Simulation result shows that the model and the algorithm are effective to the problem

A star-based method for precise flux calibration of the Chinese Space Station Telescope (CSST) slitless spectroscopic survey

The upcoming Chinese Space Station Telescope (CSST) slitless spectroscopic survey poses a challenge of flux calibration, which requires a large number of flux-standard stars. In this work, we design an uncertainty-aware residual attention network, the UaRA-net, to derive the CSST SEDs with a resolution of R = 200 over the wavelength range of 2500-10000 \AA using LAMOST normalized spectra with a resolution of R = 2000 over the wavelength range of 4000-7000 \AA. With the special structure and training strategy, the proposed model can not only provide accurate predictions of SEDs but also their corresponding errors. The precision of the predicted SEDs depends on effective temperature (Teff), wavelength, and the LAMOST spectral signal-to-noise ratios (SNRs), particularly in the GU band. For stars with Teff = 6000 K, the typical SED precisions in the GU band are 4.2%, 2.1%, and 1.5% at SNR values of 20, 40, and 80, respectively. As Teff increases to 8000 K, the precision increases to 1.2%, 0.6%, and 0.5%, respectively. The precision is higher at redder wavelengths. In the GI band, the typical SED precisions for stars with Teff = 6000 K increase to 0.3%, 0.1%, and 0.1% at SNR values of 20, 40, and 80, respectively. We further verify our model using the empirical spectra of the MILES and find good performance. The proposed method will open up new possibilities for optimal utilization of slitless spectra of the CSST and other surveys.Comment: 20 pages, 15 figures, accepted by ApJ

Ensemble-Based Simultaneous State and Parameter Estimation in a Two-Dimensional Sea-Breeze Model

© Copyright 2006 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (https://www.ametsoc.org/) or from the AMS at 617-227-2425 or [email protected] performance of the ensemble Kalman filter (EnKF) in forced, dissipative flow under imperfect-model conditions is investigated through simultaneous state and parameter estimation where the source of model error is the uncertainty in the model parameters. A two-dimensional, nonlinear, hydrostatic, nonrotating, and incompressible sea-breeze model is used for this purpose with buoyancy and vorticity as the prognostic variables and a square root filter with covariance localization is employed. To control filter divergence caused by the narrowing of parameter variance, a “conditional covariance inflation” method is devised. Up to six model parameters are subjected to estimation attempts in various experiments. While the estimation of single imperfect parameters results in error of model variables that is indistinguishable from the respective perfect-parameter cases, increasing the number of estimated parameters to six inevitably leads to a decline in the level of improvement achieved by parameter estimation. However, the overall EnKF performance in terms of the error statistics is still superior to the situation where there is parameter error but no parameter estimation is performed. In fact, compared with that situation, the simultaneous estimation of six parameters reduces the average error in buoyancy and vorticity by 40% and 46%, respectively. Several aspects of the filter configuration (e.g., observation location, ensemble size, radius of influence, and parameter variance limit) are found to considerably influence the identifiability of the parameters. The parameter-dependent response to such factors implies strong nonlinearity between the parameters and the state of the model and suggests that a straightforward spatial covariance localization does not necessarily produce optimality.GeoTechnology Research Institute National Science Foundatio

Regional Simulation of the October and November MJO Events Observed during the CINDY/DYNAMO Field Campaign at Gray Zone Resolution

This study investigates the October and November MJO events observed during the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY)/Dynamics of the MJO (DYNAMO) field campaign through cloud-permitting numerical simulations. The simulations are compared to multiple observational datasets. The control simulation at 9-km horizontal grid spacing captures the slow eastward progression of both the October and November MJO events in surface precipitation, outgoing longwave radiation, zonal wind, humidity, and large-scale vertical motion. The vertical motion shows weak ascent in the leading edge of the MJO envelope, followed by deep ascent during the peak precipitation stage and trailed by a broad second baroclinic mode structure with ascent in the upper troposphere and descent in the lower troposphere. Both the simulation and the observations also show slow northward propagation components and tropical cyclone–like vortices after the passage of the MJO active phase. Comparison with synthesized observations from the northern sounding array shows that the model simulates the passage of the two MJO events over the sounding array region well. Sensitivity experiments to SST indicate that daily SST plays an important role for the November MJO event, but much less so for the October event. Analysis of the moist static energy (MSE) budget shows that both advection and diabatic processes (i.e., surface fluxes and radiation) contribute to the development of the positive MSE anomaly in the active phase, but their contributions differ by how much they lead the precipitation peak. In comparison to the observational datasets used here, the model simulation may have a stronger surface flux feedback and a weaker radiative feedback. The normalized gross moist stability in the simulations shows an increase from near-zero values to ~0.8 during the active phase, similar to what is found in the observational datasets

Mesoscale Predictability of an Extreme Warm-Season Precipitation Event

© Copyright 2006 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (https://www.ametsoc.org/) or from the AMS at 617-227-2425 or [email protected] mesoscale model is used to investigate the mesoscale predictability of an extreme precipitation event over central Texas on 29 June 2002 that lasted through 7 July 2002. Both the intrinsic and practical aspects of warm-season predictability, especially quantitative precipitation forecasts up to 36 h, were explored through experiments with various grid resolutions, initial and boundary conditions, physics parameterization schemes, and the addition of small-scale, small-amplitude random initial errors. It is found that the high-resolution convective-resolving simulations (with grid spacing down to 3.3 km) do not produce the best simulation or forecast. It was also found that both the realistic initial condition uncertainty and model errors can result in large forecast errors for this warm-season flooding event. Thus, practically, there is room to gain higher forecast accuracy through improving the initial analysis with better data assimilation techniques or enhanced observations, and through improving the forecast model with better-resolved or -parameterized physical processes. However, even if a perfect forecast model is used, small-scale, small-amplitude initial errors, such as those in the form of undetectable random noise, can grow rapidly and subsequently contaminate the short-term deterministic mesoscale forecast within 36 h. This rapid error growth is caused by moist convection. The limited deterministic predictability of such a heavy precipitation event, both practically and intrinsically, illustrates the need for probabilistic forecasts at the mesoscales.National Science Foundation Office of Naval Researc