WRF Sensitivity for Seasonal Climate Simulations of Precipitation Fields on the CORDEX South America Domain

Dynamic numerical models of the atmosphere are the main tools used for weather and climate forecasting as well as climate projections. Thus, this work evaluated the systematic errors and areas with large uncertainties in precipitation over the South American continent (SAC) based on regional climate simulations with the weather research and forecasting (WRF) model. Ten simulations using different convective, radiation, and microphysical schemes, and an ensemble mean among them, were performed with a resolution of 50 km, covering the CORDEX-South America domain. First, the seasonal precipitation variability and its differences were discussed. Then, its annual cycle was investigated through nine sub-domains on the SAC (AMZN, AMZS, NEBN, NEBS, SE, SURU, CHAC, PEQU, and TOTL). The Taylor Diagrams were used to assess the sensitivity of the model to different parameterizations and its ability to reproduce the simulated precipitation patterns. The results showed that the WRF simulations were better than the ERA-interim (ERAI) reanalysis when compared to the TRMM, showing the added value of dynamic downscaling. For all sub-domains the best result was obtained with the ensemble compared to the satellite TRMM. The largest errors were observed in the SURU and CHAC regions, and with the greatest dispersion of members during the rainy season. On the other hand, the best results were found in the AMZS, NEBS, and TOTL regions.

Assimilating C-Band Radar Data for High-Resolution Simulations of Precipitation: Case Studies over Western Sumatra

Accurate high-resolution precipitation forecasts are critical yet challenging for weather prediction under complex topography or severe synoptic forcing. Data fusion and assimilation aimed at improving model forecasts, as one possible approach, has gained increasing attention in past decades. This study investigates the influence of the observations from a C-band Doppler radar over the west coast of Sumatra on high-resolution numerical simulations of precipitation around its vicinity under the Madden–Julian oscillation (MJO) in January and February 2018. Cases during various MJO phases were selected for simulations with an advanced research version of the weather research and forecasting (WRF) model at a cloud-permitting scale (~3 km). A 3-dimensional variational (3DVAR) data assimilation method and a hybrid three-dimensional ensemble–variational data assimilation (3DEnVAR) method, based on the NCEP Gridpoint Statistical Interpolation (GSI) assimilation system, were used to assimilate the radar reflectivity and the radial velocity data. The WRF-simulated precipitation was validated with the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data, and the fractions skill score (FSS) was calculated in order to evaluate the radar data impacts objectively. The results show improvements in the simulated precipitation with hourly radar data assimilation 6 h prior to the simulations. The modifications with assimilation were validated through the observation departure and moist convection. It was found that forecast improvements are relatively significant when precipitation is more related to local-scale convection but rather small when the background westerly wind is strong under the MJO active phase. The additional simulation experiments, under a 1- or 2-day assimilation cycle, indicate better improvements in the precipitation simulation with 3DEnVAR radar assimilation than those with the 3DVAR method.

Evaluation of Asian summer precipitation in different configurations of a high-resolution general circulation model in a range of decision-relevant spatial scales

High-resolution general circulation models (GCMs) can provide new insights into the simulated distribution of global precipitation. We evaluate how summer precipitation is represented over Asia in global simulations with a grid length of 14 km. Three simulations were performed: one with a convection parametrization, one with convection represented explicitly by the model's dynamics, and a hybrid simulation with only shallow and mid-level convection parametrized. We evaluate the mean simulated precipitation and the diurnal cycle of the amount, frequency, and intensity of the precipitation against satellite observations of precipitation from the Climate Prediction Center morphing method (CMORPH). We also compare the high-resolution simulations with coarser simulations that use parametrized convection. The simulated and observed precipitation is averaged over spatial scales defined by the hydrological catchment basins; these provide a natural spatial scale for performing decision-relevant analysis that is tied to the underlying regional physical geography. By selecting basins of different sizes, we evaluate the simulations as a function of the spatial scale. A new BAsin-Scale Model Assessment ToolkIt (BASMATI) is described, which facilitates this analysis. We find that there are strong wet biases (locally up to 72 mm d−1 at small spatial scales) in the mean precipitation over mountainous regions such as the Himalayas. The explicit convection simulation worsens existing wet and dry biases compared to the parametrized convection simulation. When the analysis is performed at different basin scales, the precipitation bias decreases as the spatial scales increase for all the simulations; the lowest-resolution simulation has the smallest root mean squared error compared to CMORPH. In the simulations, a positive mean precipitation bias over China is primarily found to be due to too frequent precipitation for the parametrized convection simulation and too intense precipitation for the explicit convection simulation. The simulated diurnal cycle of precipitation is strongly affected by the representation of convection: parametrized convection produces a peak in precipitation too close to midday over land, whereas explicit convection produces a peak that is closer to the late afternoon peak seen in observations. At increasing spatial scale, the representation of the diurnal cycle in the explicit and hybrid convection simulations improves when compared to CMORPH; this is not true for any of the parametrized simulations. Some of the strengths and weaknesses of simulated precipitation in a high-resolution GCM are found: the diurnal cycle is improved at all spatial scales with convection parametrization disabled, the interaction of the flow with orography exacerbates existing biases for mean precipitation in the high-resolution simulations, and parametrized simulations produce similar diurnal cycles regardless of their resolution. The need for tuning the high-resolution simulations is made clear. Our approach for evaluating simulated precipitation across a range of scales is widely applicable to other GCMs.

A Multi-Tool Analysis to Assess the Effectiveness of Passive Ice Protection Materials to Assist Rotorcraft Manual De-Icing

Search and rescue missions using rotorcrafts need to be reliable all year long, even in winter conditions. In some cases of deployment prior to take off, the crew may need to manually remove accumulated contaminant from the critical surfaces using tools at their disposal. However, icy contaminant may be hard to remove since the rotorcrafts critical surfaces could be cooler than the environment, thus promoting adhesion. Currently, there exists several passive ice protection materials that could reduce the ice adhesion strength and assist the manual de-icing. The aim of this paper is to propose a detailed comparative procedure to assess the ability of materials to assist the manual de-icing of rotorcrafts. The proposed procedure consists of the characterization of materials using several laboratory tests in order to determine their characteristics pertaining to wettability, their icephobic behavior, and finally their assessment under a multi-tool analysis to evaluate if they can assist. The multi-tool analysis uses different mechanical tools, which are currently used during normal operation, to execute a gradual de-icing procedure, which begins with the softest to the hardest tool using a constant number of passes or strokes, under different types of simulated precipitation. Five different materials were used to evaluate the proposed procedure: Aluminum (used as a reference), two silicone-based coatings (Nusil and SurfEllent), an epoxy-based coating (Wearlon), and finally a commercial ski wax (Swix). All of the tested materials could assist the manual de-icing, within a certain limit, when compared to the bare aluminum. However, SurfEllent was the material that obtained the best overall results. This procedure could be easily adapted to different fields of application and could be used as a development tool for the optimization and the assessment of new materials aimed to reduce ice adhesion.

Multi-Disciplinary Assessment of Napier Grass Plantation on Local Energetic, Environmental and Socioeconomic Industries: A Watershed-Scale Study in Southern Thailand

Napier grass is an energy crop that is promising for future power generation. Since Napier grass has never been planted extensively, it is important to understand the impacts of Napier grass plantations on local energetic, environmental, and socioeconomic features. In this study, the soil and water assessment tool (SWAT) model was employed to investigate the impacts of Napier grass plantation on runoff, sediment, and nitrate loads in Songkhla Lake Basin (SLB), southern Thailand. Historical data, collected between 2009 and 2018 from the U-tapao gaging station located in SLB were used to calibrate and validate the model in terms of precipitation, streamflow, and sediment. The simulated precipitation, streamflow, and sediment showed agreement with observed data, with the coefficients of determination being 0.791, 0.900, and 0.997, respectively. Subsequently, the SWAT model was applied to evaluate the impact of land use change from the baseline case to Napier grass plantation cases in abandoned areas with four different nitrogen fertilizer application levels. The results revealed that planting Napier grass decreased the average surface runoff and sediment in the watershed. A multidisciplinary assessment supporting future decision making was conducted using the results obtained from the SWAT model; these showed that Napier grass will provide enhanced benefits to hydrology and water quality when nitrogen fertilizers of 0 and 125 kgN ha−1 were applied. On the other hand, the benefits to the energy supply, farmer’s income, and CO2 reduction were highest when a nitrogen fertilization of 500 kgN ha−1 was applied. Nonetheless, planting Napier grass should be supported since it increases the energy supply and creates jobs while also reducing surface runoff, sediment yield, nitrate load, and CO2 emission.

Ensemble Forecasting Experiments Using the Breeding of Growing Modes with Perturbed Land Variables

Although land surface influences atmospheric processes significantly, insufficient studies have been conducted on the ensemble forecasts using the breeding of growing modes (BGM) with perturbed land surface variables. To investigate the practicability of perturbed land variables for ensemble forecasting, we used the ARWv3 mesoscale model to generate ensembles for an event of 24 h heavy rainfall with perturbed atmospheric and land variables by the BGM method. Results show that both atmospheric and land variables can generate initial perturbations with BGM, except that they differ in time and saturation characteristics, e.g., saturation is generally achieved in approximately 30 h with a growth rate of ~1.30 for atmospheric variables versus 102 h and growth rate of 1.02 for land variables. With the increase in precipitation, the importance of the perturbations of land variables also increases as compared to those of atmospheric variables. Moreover, the influence of the perturbations of land variables on simulated precipitation is still relatively large, although smaller than that of atmospheric variables, e.g., the spreads of perturbed atmospheric and land subsets were 7.3 and 3.8 mm, respectively. The benefits of perturbed initialisation can also be observed in terms of probability forecast. All findings indicate that the BGM method with perturbed land variables has the potential to ensemble forecasts for precipitation.

Evaluation of different convective schemes on simulation of thunderstorm event over Delhi by ARPS Model

Three difference cumulus parameterization schemes namely, Kain-Fritsch, New Kain-Fritsch and the Betts-Miller-Janjic are used to simulated convective rainfall associated with two thunderstorm events over Delhi by Advanced Regional Prediction Model (ARPS). An inter comparison of model simulated precipitation in respect of each convection scheme is made with reference to observed precipitation. The study shows that for the Delhi thunderstorm events, the Kain-Fritsch scheme provides more realistic results. This scheme is able to capture the temporal distribution of rainfall and the timely development of thunderstorm in both the cases. While the other two schemes fail to capture these features. However, the Kain-Fritsch scheme is found to overestimate the rainfall amount.

Bias Correction for Precipitation Simulated by RegCM4 over the Upper Reaches of the Yangtze River Based on the Mixed Distribution Quantile Mapping Method

Traditional multi-parameter single distribution quantile mapping (QM) methods excel in some respects in correcting climate model precipitation, but are limited in others. Multi-parameter mixed distribution quantile mapping can potentially exploit the strengths of single distribution methods and avoid their weaknesses. The correction performance of mixed distribution QM methods varies with the geographical location they are applied to and the combination of distributions that are included. This study compares multiple sets of single distribution and multi-parameter mixed distribution QM methods in order to correct the precipitation bias in the upper reaches of the Yangtze River basin (UYRB) in RegCM4 simulated precipitation. The results show that, among the selected distributions, the gamma distribution has the highest performance in the basin; explaining more than 50% of the precipitation events based on the weighting coefficients. The Gumbel distribution had the worst performance, only explaining about 10% of the precipitation events. The performance parameters, such as the root mean square error (RMSE) and the correlation coefficient (R) of the corrected precipitation, that were derived by using mixed distribution were better than those derived by using single distribution. The QM method that is based on the gamma-generalized extreme value distribution best corrected the precipitation, could reproduce the annual cycle and geographical pattern of observed precipitation, and could significantly reduce the wet bias from the RegCM4 model in the UYRB. In addition to enhancing precipitation climatology, the correction method also improved the simulation performance of the RegCM4 model for extreme precipitation events.

Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1

The performance of the Weather Research and Forecasting (WRF) model version 3.8.1 at convection-permitting scale is evaluated by means of several sensitivity simulations over southern Peru down to a grid resolution of 1 km, whereby the main focus is on the domain with 5 km horizontal resolution. Different configurations of microphysics, cumulus, longwave radiation and planetary boundary layer schemes are tested. For the year 2008, the simulated precipitation amounts and patterns are compared to gridded observational data sets and weather station data gathered from Peru, Bolivia and Brazil. The temporal correlation of simulated monthly precipitation sums against in-situ and gridded observational data show that the most challenging regions for WRF are the slopes along both sides of the Andes, i.e., elevations between 1000 and 3000 m above sea level. The pattern correlation analysis between simulated precipitation and station data suggests that all tested WRF setups perform rather poorly along the northeastern slopes of the Andes during the entire year. In the southwestern region of the domain the performance of all setups is better except for the driest period (May–September). The results of the pattern correlation to the gridded observational data sets show that all setups perform reasonably well except along both slopes during the dry season. The precipitation patterns reveal that the typical setup used over Europe is too dry throughout the entire year, and that the experiment with the combination of the single-moment 6-class microphysics scheme and the Grell–Freitas cumulus parameterization in the domains with resolutions larger than 5 km, suitable for East Africa, does not perfectly apply to other equatorial regions such as the Amazon basin in southeastern Peru. The experiment with the Stony–Brook University microphysics scheme and the Grell-Freitas cumulus parameterization tends to overestimate precipitation over the northeastern slopes of the Andes, but allows to enforce a positive feedback between the soil moisture, air temperature, relative humidity, mid-level cloud cover and finally, also precipitation. Hence, this setup is the one providing the most accurate results over the Peruvian Amazon, and particularly over the department of Madre de Dios, which is a region of interest because it is considered the biodiversity hotspot of Peru. The robustness of this particular parameterization option is backed up by similar results obtained during wet climate conditions observed in 2012.