Analyzing the Contribution of Human Mobility to Changes in Air Pollutants: Insights from the COVID-19 Lockdown in Wuhan

During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions in air pollutants, we implemented sensitivity experiments by Random Forest (RF) models, with the comparison of the contributions of meteorological conditions, human mobility, and emissions from industry and households between different periods. In addition, we conducted scenario analyses to suggest an appropriate limit for control of human mobility. Different mechanisms for air pollutants were shown in the pre-pandemic, pre-lockdown, lockdown, and post-pandemic periods. Wind speed and the Within-city Migration index, representing intra-city mobility intensity, were excluded from stepwise multiple linear models in the pre-lockdown and lockdown periods. The results of sensitivity experiments show that, in the COVID-19 lockdown period, 73.3% of the reduction can be attributed to decreased human mobility. In the post-pandemic period, meteorological conditions control about 42.2% of the decrease, and emissions from industry and households control 40.0%, while human mobility only contributes 17.8%. The results of the scenario analysis suggest that the priority of restriction should be given to human mobility within the city than other kinds of human mobility. The reduction in the NO2 concentration tends to be smaller when human mobility within the city decreases by more than 70%. A limit of less than 40% on the control of the human mobility can achieve a better effect, especially in cities with severe traffic pollution.

Simulation of monsoon circulation and cyclones with different types of orography

In this study a number of sensitivity experiments have been conducted with different types of orography in the ECMWF spectral General Circulation Model (GCM). The basic aim is to simulate the large scale features of the Indian summer monsoon and the movement of cyclones. Different types of digital filters have been used to represent the orography as close to the observed values as possible. A comparative study shows that the Lanczos filter gives the best results. Thus, the Lanczos filter has been used in subsequent sensitivity experiments. One cyclone over the Bay of Bengal during pre-monsoon month of May, and another during the must active month of August have been selected for numerical experiments. The large scale features of the monsoonal rainfall were found to be close to those observed, when standard deviation envelope orography was used. After 3 days of model integration, the rainfall distribution improved compared to the initial stages of integration. Although the predicted cyclone followed the observed track, the rate of movement was very sluggish. There was slight deepening of the systems with an increase of orography The movement of the systems was also found to be slightly faster with the enhancement of orography.

Quantitative Precipitation Forecasting Using an Improved Probability-Matching Method and Its Application to a Typhoon Event

This present study aims to explore how forecasters can quickly make accurate predictions by using various high-resolution model forecasts. Based on three high temporal-spatial resolution (3 km, hourly) numerical weather prediction models (CMA-MESO, CMA-GD, CMA-SH3) from the China Meteorological Administration (CMA), the hourly precipitation characteristics of three model within 24 h from March to September 2020 are discussed and integrated into a single, hourly, deterministic quantitative precipitation forecast (QPF) by making use of an improved weighted moving average probability-matching method (WPM). The results are as follows: (1) In non-rainstorm forecasts, CMA-MESO and CMA-GD have similar forecast abilities. However, in rainstorm forecasts, CMA-MESO has a notable advantage over the other two models. Thus, CMA-MESO is selected as a critical factor when participating in sensitivity experiments. (2) Compared with the traditional equal-weight probability-matching method (PM), the WPM improves the different grade QPF because it can effectively reduce rainfall pattern bias by making use of the weighted moving average (WMA). Additionally, the WPM threat score in rainstorm forecast similarly improved from 0.051 to 0.056, with a 9.8% increase relative to the PM. (3) The sensitivity experiments show that an optimal rainfall intensity score (WPM-best) can further improve the QPF and overcome all single models in both rainstorm and non-rainstorm forecasts, and the WPM-best has a rainstorm threat score skill of 0.062, with an increase of 21.6% compared with the PM. The performance of the WPM-best will be better if the precipitation intensity is stronger and the valid forecast periods is longer. It should be noted that there is no need to select models before using the WPM-best method, because WPM-best can give a very low weight to the less-skillful model in a more objective way. (4) The improved WPM method is also applied to investigate the heavy-rainfall case induced by typhoon Mekkhala (2020), where the improved WPM technique significantly improves rainstorm forecasting ability compared with a single model.

Earth System Model Parameter Adjustment Using a Green's Functions Approach

We demonstrate the practicality and effectiveness of using a Green's functions estimation approach for adjusting uncertain parameters in an Earth System Model (ESM). This estimation approach had previously been applied to an intermediate-complexity climate model and to individual ESM components, e.g., ocean, sea-ice, or carbon-cycle components. Here, the Green's functions approach is applied to a state-of-the-art ESM that comprises a global atmosphere-land configuration of the Goddard Earth Observing System (GEOS) coupled to an ocean and sea-ice configuration of the Massachusetts Institute of Technology general circulation model (MITgcm). Horizontal grid spacing is approximately 110 km for GEOS and 37–110 km for MITgcm. In addition to the reference GEOS-MITgcm simulation, we carry out a series of model sensitivity experiments, in which 20 uncertain parameters are perturbed. These control parameters can be used to adjust sea-ice, microphysics, turbulence, radiation, and surface schemes in the coupled simulation. We define eight observational targets: sea-ice fraction, net surface shortwave radiation, downward longwave radiation, near-surface temperature, sea surface temperature, sea surface salinity, and ocean temperature and salinity at 300 m. We applied the Green's functions approach to optimize the values of the 20 control parameters so as to minimize a weighted least-squares distance between the model and the eight observational targets. The new experiment with the optimized parameters resulted in a total cost reduction of 9 % relative to a simulation that had already been adjusted using other methods. The optimized experiment attained a balanced cost reduction over most of the observational targets. We also report on results from a set of sensitivity experiments that are not used in the final optimized simulation but helped explore options and guided the optimization process. These experiments include an assessment of sensitivity to the number of control parameters and to the selection of observational targets and weights in the cost function. Based on these sensitivity experiments, we selected a specific definition for the cost function. The sensitivity experiments also revealed a decreasing overall cost as the number of control variables was increased. In summary, we recommend using the Green's functions estimation approach as an additional fine-tuning step in the model development process. The method is not a replacement for modelers' experience in choosing and adjusting sensitive model parameters. Instead, it is an additional practical and effective tool for carrying out final adjustments of uncertain ESM parameters.

Sensitivity Experiments on the Role of Water Vapor in the Eastward Propagation of MJO

In this study, we employed the nudging assimilation of the Weather Research and Forecasting (WRF) model to conduct a set of sensitivity experiments on the role of water vapor in the Madden-Julian Oscillation (MJO) eastward propagation, focusing on the eastward propagating 30-60d low-frequency component in the tropical atmosphere from the Indian Ocean to the western Pacific Ocean during September-November 2004. Using 11 different cumulus parameterization schemes, the simulation results show that the ability of the regional climate model in simulating the MJO eastward propagation is sensitive to the cumulus scheme: A suitable scheme can well reproduce the MJO eastward propagation characteristics, while most schemes show no skill for the MJO eastward propagation. When the water vapor in the model domain was assimilated using reanalysis data with nudging technique, we found that the low-frequency evolution of the tropical zonal wind exhibits MJO features well, and the low-frequency phase of water vapor is ahead of the zonal wind by about 6-7 days, which suggests that the atmospheric water-vapor distribution is the key factor for the eastward propagation of the MJO, and the effect of water-vapor field via affecting the atmospheric stability. When the atmospheric temperature assimilation was conducted, there was almost no improvement in the skill of MJO simulation.

Influence of water vapor distribution on the simulated track of Typhoon Hato (2017)

Predicting tropical cyclone (TCs) tracks is a primary concern in TC forecasting. Some TCs appear to move in a direction favorable for their development, beyond the influence of the steering flow. Thus, we hypothesize that TCs move toward regions with high water-vapor content in the lower atmosphere. In this study, four numerical experiments, including a control experiment and three sensitivity experiments, were performed using the Weather Research and Forecasting Model, to analyze the relationship between water vapor distribution and the track of Severe Typhoon Hato (2017). Observations validated the features reproduced in the control experiment. The sensitivity experiments were conducted to explore variations in the TC track under different water vapor environments. Results indicate that the horizontal distribution of water-vapor content exerted a greater impact on the TC track than the steering flow when both factors were significant. Further analysis revealed that the TC’s movement vector was between the direction of the steering flow and the direction toward the peak of vorticity increasing area. The peaks of vorticity increasing area were close to the peaks of water vapor increasing area, which also proved the effect of water vapor distribution on the TC track. These results are expected to improve TC track analysis and forecasting.

Vertical viscosity coefficient increased for high-resolution modeling of the Tsushima/Korea Strait

This study clarifies the necessity of an extraordinary large coefficient of vertical viscosity for dynamical ocean modeling in a shallow and narrow strait with complex bathymetry. Sensitivity experiments and objective analyses imply that background momentum viscosity is at the order of 100 cm2/s, while tracer diffusivity estimates are on the order of 0.1 cm2/s. The physical interpretation of these estimates is also discussed in the last part of this paper. To obtain reliable solutions, this study introduces cyclic application of the dynamical response to each parameter to minimize the number of long-term sensitivity experiments. The recycling Green’s function method yields weaker bottom friction and enhanced latent heat flux simultaneously with the increased viscosity in high-resolution modeling of the Tsushima/Korea Strait.