Rainfall prediction over Ambon Meteorological Station using Multi-Physics Ensemble WRF-ARW

One of the methods to create good forecast using WRF-ARW modelling is tuning the parameterization. However, this method cannot provide rainfall event probability. Current research result revealed that it was able to simulate and forecast some weather parameters. However, based on the verification results, there were some weather parameters which still had low accuracy. Due to such low accuracy on some weather parameters, the authors were interested in performing post-processing methods in forecasting the weather during extreme weather at Pattimura Ambon Meteorological Station. In this study, we employed multi-physics ensemble prediction system (MEPS) by combining 20 WRF-ARW parameterization schemas, which were processed to obtain the ensemble mean, ensemble spread, and basic probability to get the uncertainty from each weather parameters. Verification process was done by using spreads, skill method and ROC curves. It was discovered that MEPS products have a better skill compared to the forecast control, the correlation value of MEPS products is larger and has the lowest error value. In addition, the result of ROC curves shows that the MEPS has an ability to predict weather condition during cloudy and extreme rain.

Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models

In the event of an accidental or intentional hazardous material release in the atmosphere, researchers often run physics-based atmospheric transport and dispersion models to predict the extent and variation of the contaminant spread. These predictions are imperfect due to propagated uncertainty from atmospheric model physics (or parameterizations) and weather data initial conditions. Ensembles of simulations can be used to estimate uncertainty, but running large ensembles is often very time consuming and resource intensive, even using large supercomputers. In this paper, we present a machine-learning-based method which can be used to quickly emulate spatial deposition patterns from a multi-physics ensemble of dispersion simulations. We use a hybrid linear and logistic regression method that can predict deposition in more than 100,000 grid cells with as few as fifty training examples. Logistic regression provides probabilistic predictions of the presence or absence of hazardous materials, while linear regression predicts the quantity of hazardous materials. The coefficients of the linear regressions also open avenues of exploration regarding interpretability—the presented model can be used to find which physics schemes are most important over different spatial areas. A single regression prediction is on the order of 10,000 times faster than running a weather and dispersion simulation. However, considering the number of weather and dispersion simulations needed to train the regressions, the speed-up achieved when considering the whole ensemble is about 24 times. Ultimately, this work will allow atmospheric researchers to produce potential contamination scenarios with uncertainty estimates faster than previously possible, aiding public servants and first responders.

Multi-Model Multi-Physics Ensemble: A Futuristic Way to Extended Range Prediction System

In an endeavor to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version. The exiting operational extended range prediction system (ERPv1) combines the coupled, and its bias-corrected sea-surface temperature forced atmospheric model running at two resolutions with perturbed initial condition ensemble. This system had accomplished important goals on the sub-seasonal scale skillful forecast; however, the skill of the system is limited only up to 2 weeks. The next version of this ERP system is seamless in resolution and based on a multi-physics multi-model ensemble (MPMME). Similar to the earlier version, this system includes coupled climate forecast system version 2 (CFSv2) and atmospheric global forecast system forced with real-time bias-corrected sea-surface temperature from CFSv2. In the newer version, model integrations are performed six times in a month for real-time prediction, selecting the combination of convective and microphysics parameterization schemes. Additionally, more than 15 years hindcast are also generated for these initial conditions. The preliminary results from this system demonstrate appreciable improvements over its predecessor in predicting the large-scale low variability signal and weekly mean rainfall up to 3 weeks lead. The subdivision-wise skill analysis shows that MPMME performs better, especially in the northwest and central parts of India.

Downscaling of a seasonal ensemble forecast at the convection-permitting resolution over the Horn of Africa using the WRF model

<p>Studies have shown the benefits of convection-permitting downscaling at the seasonal scale using limited-area models. To evaluate the performance with real forecasts as boundary conditions, four members of the SEAS5 global ensemble were dynamically downscaled over Ethiopia during June, July, and August 2018 at a 3-km resolution. We used a multi‐physics ensemble based on the WRF model to compare the effects of boundary conditions and physics <span><span>parametrization</span></span> producing 16 ensemble members. With ECMWF analyses as a reference, SEAS5 averaged to a +0.17°C bias over Ethiopia whereas WRF resulted in +1.14°C. With respect to precipitation, the WRF model simulated 264 mm compared to 248 mm for SEAS5 and 236 mm for GPM-IMERG. The maximum northward extension of the tropical rain belt decreased by about 2° in both models. Downscaling enhanced the ensemble spread in precipitation by 60% on average, correcting the SEAS5 underdispersion. The WRF ensemble spread over Ethiopia was mostly generated by the perturbed boundary conditions, as their effect is often 50% larger than the physics‐induced variability. The results indicate that boundary condition perturbations are necessary, although not always sufficient, to generate the right amount of ensemble spread in a limited-area model with complex topography. The next step is to use specific methods to calculate the added value provided by the downscaling.</p>

Evaluating parametric sensitivity of climate feedbacks in CNRM-CM6

<p>The global surface temperature response to CO2 doubling (Equilibrium Climate Sensitivity or ECS) is a key uncertain parameter determining the extent of future climate change. Sherwood et al. (2020) estimated the ECS to be within [2.6K - 4.5K], but in the Coupled Model Intercomparison Project phase 6 (CMIP6), 1/3 of the General Circulation Models (GCMs) show ECS exceeding 4.5K (Zelinka et al., 2020). CNRM-CM6-1 is one of these models, with an ECS of 4.9K. In this paper, we sampled 30 atmospheric parameters of CNRM-CM6-1 and produced a Perturbed Physics Ensemble (PPE) of atmospheric-only simulations to explore the feedback parameters diversity and the climatological plausibility of the members. This PPE showed a comparable  range of feedback parameters to the multi-model archive, from 0.8 W.m-2/K to 1.8 W.m-2/K. Emulators of climatological performance and feedback parameters were used together with  observational datasets to search for optimal model configurations conditional on different net climate feedbacks. The climatological constraints considered here did not themselves rule out the higher end ECS values of 5K and above. An optimal subset of parameter configurations were chosen to sample the range of ECS allowing the assessment of feedback constraints in future fully coupled experiments.</p><p> </p><p><strong>References :</strong></p><p>Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., ... & Zelinka, M. D. (2020). An assessment of Earth's climate sensitivity using multiple lines of evidence. Reviews of Geophysics, 58(4), e2019RG000678.</p><p>Zelinka, M. D., Myers, T. A., McCoy, D. T., Po‐Chedley, S., Caldwell, P. M., Ceppi, P., ... & Taylor, K. E. (2020). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47(1), e2019GL085782.</p><p><br><br></p>

0.5 Degree: A Turning point

<p>Although the Caribbean region is considered amongst the most vulnerable to the impacts of climate and climate change, there are very few regional studies or studies matching the regions small scale and size that evaluate or quantify the impacts of these future changes.  The absence becomes even more stark when the long-term temperature goals (LTTGs) of 1.5°C, 2.0°C and 2.5°C above pre-industrial warming levels are considered. By selecting, validating and downscaling a subset of the Hadley Centre’s 17-member Perturbed Physics Ensemble for the Quantifying Uncertainty in Model Predictions (QUMP) project, future changes for both the LTTGs as well as mid and end of century are evaluated, for the entire Caribbean and its six (6) sub-regional zones. Showing distinct and significant sub-regional variations, on average the Caribbean was found to be 2.1°C (>4°C) warmer and 40% (70%) drier by mid-century (end of century). Analysis of the LTTGS shows that the region surpasses lowest target, 1.5 °C, before the end of the 2020’s and experiences progressive warming that spread equatorward as successive thresholds are attained 2.0°C (2030’s) and 2.5°C (2050´s). The far western, the southern and the eastern Caribbean are found to be up to 50% drier at 1.5°C, with intensifications noted for changes at 2.0°C with a reversal of a wet tendency in the north and central Caribbean. The sub-regional variations that exist shows that although the Caribbean lags the globe in its attainment of the LTTGs some of its six subregions are more comparable to the global than the Caribbean mean with the transition from 1.5°C to 2.0°C seeming to represent a turning point for the Caribbean.</p>

Generating Projections for the Caribbean at 1.5, 2.0, and 2.5 °C from a High-Resolution Ensemble

Six members of the Hadley Centre’s Perturbed Physics Ensemble for the Quantifying Uncertainty in Model Predictions (QUMP) project are downscaled using the PRECIS (Providing Regional Climates for Impact Studies) RCM (Regional Climate Model). Climate scenarios at long-term temperature goals (LTTGs) of 1.5, 2.0, and 2.5 °C above pre-industrial warming levels are generated for the Caribbean and six sub-regions for annual and seasonal timescales. Under a high emissions scenario, the LTTGs are attained in the mid-2020s, end of the 2030s, and the early 2050s, respectively. At 1.5 °C, the region is slightly cooler than the globe, land areas warmer than ocean, and for the later months, the north is warmer than the south. The far western and southern Caribbean including the eastern Caribbean island chain dry at 1.5 °C (up to 50%). At 2.0 °C, the warming and drying intensify and there is a reversal of a wet tendency in parts of the north Caribbean. Drying in the rainfall season accounts for much of the annual change. There is limited further intensification of the region-wide drying at 2.5 °C. Changes in wind strength in the Caribbean low-level jet region may contribute to the patterns seen. There are implications for urgent and targeted adaptation planning in the Caribbean.