ALBERT

Description: Accurate simulation of planetary boundary layer height (PBLH) is key to greenhouse gas emission estimation, air quality prediction, and weather forecasting. This paper describes an extensive performance assessment of several Weather Research and Forecasting (WRF) Model configurations in which novel observations from ceilometers, surface stations, and a flux tower were used to study their ability to reproduce the PBLH and the impact that the urban heat island (UHI) has on the modeled PBLHs in the greater Washington, D.C., area. In addition, CO2 measurements at two urban towers were compared with tracer transport simulations. The ensemble of models used four PBL parameterizations, two sources of initial and boundary conditions, and one configuration including the building energy parameterization urban canopy model. Results have shown low biases over the whole domain and period for wind speed, wind direction, and temperature, with no drastic differences between meteorological drivers. We find that PBLH errors are mostly positively correlated with sensible heat flux errors and that modeled positive UHI intensities are associated with deeper modeled PBLs over the urban areas. In addition, we find that modeled PBLHs are typically biased low during nighttime for most of the configurations with the exception of those using the MYNN parameterization, and these biases directly translate to tracer biases. Overall, the configurations using the MYNN scheme performed the best, reproducing the PBLH and CO2 molar fractions reasonably well during all hours and thus opening the door to future nighttime inverse modeling.

Description: Tropical cyclones (TCs) rank among the most costly natural disasters in the United States, and accurate forecasts of track and intensity are critical for emergency response. Intensity guidance has improved steadily but slowly, as processes which drive intensity change are not fully understood. Because most TCs develop far from land-based observing networks, geostationary satellite imagery is critical to monitor these storms. However, these complex data can be challenging to analyze in real time, and off-the-shelf machine learning algorithms have limited applicability on this front due to their “black box” structure. This study presents analytic tools that quantify convective structure patterns in infrared satellite imagery for over-ocean TCs, yielding lower-dimensional but rich representations that support analysis and visualization of how these patterns evolve during rapid intensity change. The proposed ORB feature suite targets the global Organization, Radial structure, and Bulk morphology of TCs. By combining ORB and empirical orthogonal functions, we arrive at an interpretable and rich representation of convective structure patterns that serve as inputs to machine learning methods. This study uses the logistic lasso, a penalized generalized linear model, to relate predictors to rapid intensity change. Using ORB alone, binary classifiers identifying the presence (versus absence) of such intensity change events can achieve accuracy comparable to classifiers using environmental predictors alone, with a combined predictor set improving classification accuracy in some settings. More complex nonlinear machine learning methods did not perform better than the linear logistic lasso model for current data.

Description: Realistically representing the land–atmosphere interactions during persistent cold-air pools (PCAPs) is critical in simulating the strength of PCAPs, where uncertainties in simulating the PCAP strength will impact the ability to model the poor air quality. To quantify the model performance for land–atmosphere exchange, measurements of surface turbulent and radiative energy fluxes during two PCAPs, one weak and one strong, in Utah were compared with simulations from the Weather Research and Forecasting (WRF) Model. The results show that the WRF Model simulated the surface energy fluxes well in the weak PCAP case and that the performance degraded in the strong PCAP case. The significantly overestimated surface sensible heat flux H and latent heat flux (LE) in the strong PCAP were related, in part, to the overestimated net radiation and soil moisture and unsuitable turbulence parameterizations. The simulation using the Mellor–Yamada–Nakanishi–Niino planetary boundary layer scheme produced the least bias in both net radiation and surface turbulent fluxes for the strong PCAP case, which is expected because of the local higher-order (2.5) turbulence closure scheme. The surface exchange coefficient (CH), a crucial variable used to calculate H, was overall overestimated by the WRF Model. The underestimation of the nondimensional vertical temperature gradient in the Monin–Obukhov stability function was responsible for the overestimated CH, where the stability functions deviate significantly from expected values from observations for the stable atmospheric boundary layer. Our study highlights the need to improve the flux–profile parameterizations under stable conditions over complex terrain by including impacts due to mountainous terrain, such as surface radiative flux divergence and the diurnal mountain wind system.

Description: This study analyzes the microphysics and precipitation pattern of Hurricanes Harvey (2017) and Florence (2018) in both the eyewall and outer rainband regions. From the retrievals by a satellite red–green–blue scheme, the outer rainbands show a strong convective structure while the inner eyewall has less convective vigor (i.e., weaker upper-level reflectivities and electrification), which may be related to stronger vertical wind shear that hinders fast vertical motions. The WSR-88D column-vertical profiles further confirm that the outer rainband clouds have strong vertical motion and large ice-phase hydrometeor formation aloft, which correlates well with 3D Lightning Mapping Array source counts in height and time. From the results from this study, it is determined that the inner eyewall region is dominated by warm rain, whereas the external rainband region contains intense mixed-phase precipitation. External rainbands are defined here as those that reside outside of the main hurricane circulation, associated with surface tropical storm wind speeds. The synergy of satellite and radar dual-polarization parameters is instrumental in distinguishing between the key microphysical features of intense convective rainbands and the warm-rain-dominated eyewall regions within the hurricanes. Substantial amounts of ice aloft and intense updrafts in the external rainbands are indicative of heavy surface precipitation, which can have important implications for severe weather warnings and quantitative precipitation forecasts. The novel part of this study is to combine ground-based radar measurement with satellite observations to study hurricane microphysical structure from surface to cloud top so as to fill in the gaps between the two observational techniques.

Description: The objective of this study is to propose and evaluate a set of modifications to enhance a machine-learning-based method for forecasting day-ahead solar irradiation. To assess the proposed modifications, they were implemented in an initial forecast method, and their effectiveness was analyzed using two years of data on a national scale in Japan. In addition, the accuracy of the modified method was compared with one of the forecast methods for solar irradiation used by the Japan Meteorological Agency (JMA), namely, the mesoscale model (MSM). Such forecasts were made publicly available only recently, which makes this study one of the first ones to compare them with machine-learning-based forecasts. The annual root-mean-square error (RMSE) of local forecasts of the JMA-MSM varied from 0.1 to 0.14 kW h m−2; the regional equivalent varied from 0.062 to 0.091 kW h m−2. In comparison with these results, the modified model achieved an average RMSE reduction of 7.5% on the local scale and 16% on the regional scale. The modified model also had a skill score that was 23% higher than that of the JMA model. Furthermore, the performance of the JMA model had strong spatial and seasonal dependencies, which were reduced in the machine-learning-based forecasts. The results show that the proposed modifications are effective in reducing large forecasts errors, but they cannot compensate for situations in which the input data used to make the forecasts are highly inaccurate.

Description: The city of Jeddah, Saudi Arabia, is characterized by a hot and arid desert climate. On occasion, however, extreme precipitation events have led to flooding that caused extensive damage to human life and infrastructure. This study investigates the effect of incorporating an urban canopy model and urban land cover when simulating severe weather events over Jeddah using the Weather Research and Forecasting (WRF) Model at a convective-permitting scale (1.5-km resolution). Two experiments were conducted for 10 heavy rainfall events associated with the dominant large-scale patterns favoring convection over Jeddah: (i) an “urban” experiment that included the urban canopy model and modern-day land cover and (ii) a “desert” experiment that replaced the city area with its presettlement, natural land cover. The results suggest that urbanization plays an important role in modifying rainfall around city area. The urban experiment enhances the amount of rainfall by 26% on average over the Jeddah city area relative to the desert experiment in these extreme events. The changes in model-simulated precipitation are primarily tied to a nocturnal heat-island effect that modifies the planetary boundary layer and atmospheric instability of the convective events.

Description: Heavy rainfall and strong wind are the two main sources of disasters that are caused by tropical cyclones (TCs), and typhoons with different characteristics may induce different agricultural losses. Traditionally, the classification of typhoon intensity has not considered the amount of rainfall. Here, we propose a novel approach to calculate the typhoon type index (TTI). A positive TTI represents a “wind type” typhoon, where the overall damage in a certain area from TCs is dominated by strong wind. On the other hand, a negative TTI represents a “rain type” typhoon, where the overall damage in a certain area from TCs is dominated by heavy rainfall. From the TTI, the vulnerability of crop losses from different types of typhoons can be compared and explored. For example, Typhoon Kalmaegi (2008) was classified as a rain-type typhoon (TTI = −1.22). The most affected crops were oriental melons and leafy vegetables. On the contrary, Typhoon Soudelor (2015) was classified as a significant wind-type typhoon in most of Taiwan (TTI = 1.83), and the damaged crops were mainly bananas, bamboo shoots, pomelos, and other crops that are easily blown off by strong winds. Through the method that is proposed in this study, we can understand the characteristics of each typhoon that deviate from the general situation and explore the damages that are mainly caused by strong winds or heavy rainfall at different locations. This approach can provide very useful information that is important for the disaster analysis of different agricultural products.

Description: The demand of city planners for quantitative information on the impact of climate change on the urban environment is increasing. However, such information is usually extracted from decadelong climate projections generated with global or regional climate models (RCMs). Because of their coarse resolution and unsuitable physical parameterization, however, their model output is not adequate to be used at city scale. A full dynamical downscaling to city level, on the other hand, is computationally too expensive for climatological time scales. A statistical–dynamical computationally inexpensive method is therefore proposed that approximates well the behavior of the full dynamical downscaling approach. The approach downscales RCM simulations using the combination of an RCM at high resolution (H-RES) and a land surface model (LSM). The method involves the setup of a database of urban signatures by running an H-RES RCM with and without urban parameterization for a relatively short period. Using an analog approach, these signatures are first selectively added to the long-term RCM data, which are then used as forcing for an LSM using an urban parameterization in a stand-alone mode. A comparison with a full dynamical downscaling approach is presented for the city of Brussels, Belgium, for 30 summers with the combined ALADIN–AROME model (ALARO-0) coupled to the Surface Externalisée model (SURFEX) as H-RES RCM and SURFEX as LSM. The average bias of the nocturnal urban heat island during heat waves is vanishingly small, and the RMSE is strongly reduced. Not only is the statistical–dynamical approach able to correct the heat-wave number and intensities, it can also improve intervariable correlations and multivariate and temporally correlated indices, such as Humidex.

Description: Surface temperature is one of the key parameters for estimating regional evapotranspiration (ET) based on the Surface Energy Balance System (SEBS) model using remote sensing data. However, continuous daily remote sensing surface temperature data are often not available due to the weather and environmental conditions. This paper proposed a scheme to obtain reliable ET that estimating ET using WRF-simulated surface skin temperature (TSK) and then modifying the deviation using the normalized difference vegetation index (NDVI). This study aims to explore whether the model data can be a viable option when the continuous-time-series remote sensing surface temperature is missing for estimating ET. Comparison results show that the correlation between WRF TSK and the measured temperature of the 2-cm soil ( T s) is better than MODIS land surface temperature (LST) in the study area, while the correlation between MODIS LST and the measured surface radiation temperature (IRT) is better than WRF TSK. The MODIS LST is significantly higher than T s, and the WRF TSK is closer to T s than MODIS LST. However, the ET calculated using WRF TSK was not good, exhibiting relatively high ET in the whole area and a poor correlation with the measurements, whereby R2, RMSE, and the percent bias (PBIAS) were equal to 0.1256, 5.2783 mm, and −202.17%, respectively. According to the principle of land surface process simulation in WRF, this paper proposes using NDVI to modify ET calculated using TSK. The comparison between the modified ET and the measurements exhibited a relatively good correlation, with R2 = 0.7532, RMSE = 1.0993 mm, and PBIAS = −17.9%. Therefore, the model surface temperature data can be used to estimate continuous-time-series regional ET when NDVI is used to modify the deviation, which indicates the surface temperature data simulated by the WRF Model can become the optional data for estimating ET and compensate for the shortcoming of poor time continuity of remote sensing data, further expanding the application prospects of meteorological model data in the remote sensing field.

Description: Satellite retrievals strive to exploit the information contained in thousands of channels provided by hyperspectral sensors and show promise in providing a gain in computational efficiency over current radiance assimilation methods by transferring computationally expensive radiative transfer calculations to retrieval providers. This paper describes the implementation of a new approach based on the transformation proposed in 2008 by Migliorini et al., which reduces the impact of the a priori information in the retrievals and generates transformed retrievals (TRs) whose assimilation does not require knowledge of the hyperspectral instruments characteristics. Significantly, the results confirm both the viability of Migliorini’s approach and the possibility of assimilating data from different hyperspectral satellite sensors regardless of the instrument characteristics. The Weather Research and Forecasting (WRF) Model’s Data Assimilation (WRFDA) 3-h cycling system was tested over the central North Pacific Ocean, and the results show that the assimilation of TRs has a greater impact in the characterization of the water vapor distribution than on the temperature field. These results are consistent with the knowledge that temperature field is well constrained by the initial and boundary conditions of the Global Forecast System (GFS), whereas the water vapor distribution is less well constrained in the GFS. While some preliminary results on the comparison between the assimilation with and without TRs in the forecasting system are presented in this paper, additional work remains to explore the impact of the new assimilation approach on forecasts and will be provided in a follow-up publication.