Influence of cloud microphysics schemes on weather model predictions of heavy precipitation

Cloud microphysics is one of the major sources of uncertainty in numerical weather prediction models. In this work, the ability of a numerical weather prediction model to correctly predict high-impact weather events, i.e., hail and heavy rain, using different cloud microphysics schemes is evaluated statistically. Polarimetric C-band radar observations over 30 convection days are used as observation dataset. Simulations are made using the regional-scale Weather Research and Forecasting Model (WRF) with five microphysical schemes of varying complexity (double moment, spectral bin (SBM), and particle property prediction (P3)). Statistical characteristics of heavy rain and hail events of varying intensities are compared between simulations and observations. All simulations, regardless of the microphysical scheme, predict heavy rain events that cover larger average areas than those observed by radar. The frequency of these heavy rain events is similar to radar-measured heavy rain events, but still scatters by a factor of 2 around the observations, depending on the microphysical scheme. The model is generally unable to simulate extreme hail events with reflectivity thresholds of 55 dBZ and higher, although they have been observed by radar during the evaluation period. For slightly weaker hail/graupel events, only the P3 model is able to reproduce the observed statistics. Analysis of the raindrop size distribution in combination with the model mixing ratio shows that the P3, Thompson 2-mom, and Thompson aerosol-aware models produce large raindrops too frequently, and the SBM model misses large rain and graupel particles.</p

The effect of dry and wet deposition of condensable vapors on secondary organic aerosols concentrations over the continental US

The effect of dry and wet deposition of semi-volatile organic compounds (SVOCs) in the gas phase on the concentrations of secondary organic aerosol (SOA) is reassessed using recently derived water solubility information. The water solubility of SVOCs was implemented as a function of their volatility distribution within the WRF-Chem regional chemistry transport model, and simulations were carried out over the continental United States for the year 2010. Results show that including dry and wet removal of gas-phase SVOCs reduces annual average surface concentrations of anthropogenic and biogenic SOA by 48 and 63% respectively over the continental US. Dry deposition of gas-phase SVOCs is found to be more effective than wet deposition in reducing SOA concentrations (−40 vs. −8% for anthropogenics, and −52 vs. −11% for biogenics). Reductions for biogenic SOA are found to be higher due to the higher water solubility of biogenic SVOCs. The majority of the total mass of SVOC + SOA is actually deposited via the gas phase (61% for anthropogenics and 76% for biogenics). Results are sensitive to assumptions made in the dry deposition scheme, but gas-phase deposition of SVOCs remains crucial even under conservative estimates. Considering reactivity of gas-phase SVOCs in the dry deposition scheme was found to be negligible. Further sensitivity studies where we reduce the volatility of organic matter show that consideration of gas-phase SVOC removal still reduces average SOA concentrations by 31% on average. We consider this a lower bound for the effect of gas-phase SVOC removal on SOA concentrations. A saturation effect is observed for Henry's law constants above 108 M atm−1, suggesting an upper bound of reductions in surface level SOA concentrations by 60% through removal of gas-phase SVOCs. Other models that do not consider dry and wet removal of gas-phase SVOCs would hence overestimate SOA concentrations by roughly 50%. Assumptions about the water solubility of SVOCs made in some current modeling systems (H* = H* (CH3COOH); H* = 105 M atm−1; H* = H* (HNO3)) still lead to an overestimation of 35%/25%/10% compared to our best estimate

BEATBOX v1.0: Background Error Analysis Testbed with Box Models

The Background Error Analysis Testbed (BEATBOX) is a new data assimilation framework for box models. Based on the BOX Model eXtension (BOXMOX) to the Kinetic Pre-Processor (KPP), this framework allows users to conduct performance evaluations of data assimilation experiments, sensitivity analyses, and detailed chemical scheme diagnostics from an observation simulation system experiment (OSSE) point of view. The BEATBOX framework incorporates an observation simulator and a data assimilation system with the possibility of choosing ensemble, adjoint, or combined sensitivities. A user-friendly, Python-based interface allows for the tuning of many parameters for atmospheric chemistry and data assimilation research as well as for educational purposes, for example observation error, model covariances, ensemble size, perturbation distribution in the initial conditions, and so on. In this work, the testbed is described and two case studies are presented to illustrate the design of a typical OSSE experiment, data assimilation experiments, a sensitivity analysis, and a method for diagnosing model errors. BEATBOX is released as an open source tool for the atmospheric chemistry and data assimilation communities

Evaluation of convective cloud microphysics in numerical weather prediction models with dual-wavelength polarimetric radar observations: methods and examples

The representation of cloud microphysical processes contributes substantially to the uncertainty of numerical weather simulations. In part, this is owed to some fundamental knowledge gaps in the underlying processes due to the difficulty of observing them directly. On the path to closing these gaps, we present a setup for the systematic characterization of differences between numerical weather model and radar observations for convective weather situations. Radar observations are introduced which provide targeted dual-wavelength and polarimetric measurements of convective clouds with the potential to provide more detailed information about hydrometeor shapes and sizes. A convection-permitting regional weather model setup is established using five different microphysics schemes (double-moment, spectral bin ("Fast Spectral Bin Microphysics", FSBM), and particle property prediction (P3)). Observations are compared to hindcasts which are created with a polarimetric radar forward simulator for all measurement days. A cell-tracking algorithm applied to radar and model data facilitates comparison on a cell object basis. Statistical comparisons of radar observations and numerical weather model runs are presented on a data set of 30 convection days. In general, simulations show too few weak and small-scale convective cells. Contoured frequency by altitude diagrams of radar signatures reveal deviations between the schemes and observations in ice and liquid phase. Apart from the P3 scheme, high reflectivities in the ice phase are simulated too frequently. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions, producing too large or too dense graupel particles. Comparison of polarimetric radar signatures reveals issues of all schemes except the FSBM to correctly represent rain particle size distributions

Achieving Brazil's deforestation target will reduce fire and deliver air quality and public health benefits

Climate, deforestation, and forest fires are closely coupled in the Amazon, but models of fire that include these interactions are lacking. We trained machine learning models on temperature, rainfall, deforestation, land-use, and fire data to show that spatial and temporal patterns of fire in the Amazon are strongly modified by deforestation. We find that fire count across the Brazilian Amazon increases by 0.44 percentage points for each percentage point increase in deforestation rate. We used the model to predict that the increased deforestation rate in the Brazilian Amazon from 2013 to 2020 caused a 42% increase in fire counts in 2020. We predict that if Brazil had achieved the deforestation target under the National Policy on Climate Change, there would have been 32% fewer fire counts across the Brazilian Amazon in 2020. Using a regional chemistry-climate model and exposure-response associations, we estimate that the improved air quality due to reduced smoke emission under this scenario would have resulted in 2300 fewer deaths due to reduced exposure to fine particulate matter. Our analysis demonstrates the air quality and public health benefits that would accrue from reducing deforestation in the Brazilian Amazon

Sensitivity of air pollution exposure and disease burden to emission changes in China using machine learning emulation

Machine learning models can emulate chemical transport models, reducing computational costs and enabling more experimentation. We developed emulators to predict annual−mean fine particulate matter (PM(2.5)) and ozone (O(3)) concentrations and their associated chronic health impacts from changes in five major emission sectors (residential, industrial, land transport, agriculture, and power generation) in China. The emulators predicted 99.9% of the variance in PM(2.5) and O(3) concentrations. We used these emulators to estimate how emission reductions can attain air quality targets. In 2015, we estimate that PM(2.5) exposure was 47.4 μg m(−3) and O(3) exposure was 43.8 ppb, associated with 2,189,700 (95% uncertainty interval, 95UI: 1,948,000–2,427,300) premature deaths per year, primarily from PM(2.5) exposure (98%). PM(2.5) exposure and the associated disease burden were most sensitive to industry and residential emissions. We explore the sensitivity of exposure and health to different combinations of emission reductions. The National Air Quality Target (35 μg m(−3)) for PM(2.5) concentrations can be attained nationally with emission reductions of 72% in industrial, 57% in residential, 36% in land transport, 35% in agricultural, and 33% in power generation emissions. We show that complete removal of emissions from these five sectors does not enable the attainment of the WHO Annual Guideline (5 μg m(−3)) due to remaining air pollution from other sources. Our work provides the first assessment of how air pollution exposure and disease burden in China varies as emissions change across these five sectors and highlights the value of emulators in air quality research

Residential energy use emissions dominate health impacts from exposure to ambient particulate matter in India

Exposure to ambient fine particulate matter (PM2.5) is a leading contributor to diseases in India. Previous studies analysing emission source attributions were restricted by coarse model resolution and limited PM2.5 observations. We use a regional model informed by new observations to make the first high-resolution study of the sector-specific disease burden from ambient PM2.5 exposure in India. Observed annual mean PM2.5 concentrations exceed 100 mu g m(-3) and are well simulated by the model. We calculate that the emissions from residential energy use dominate (52%) population-weighted annual mean PM2.5 concentrations, and are attributed to 511,000 (95UI: 340,000-697,000) premature mortalities annually. However, removing residential energy use emissions would avert only 256,000 (95UI: 162,000-340,000), due to the non-linear exposure-response relationship causing health effects to saturate at high PM2.5 concentrations. Consequently, large reductions in emissions will be required to reduce the health burden from ambient PM2.5 exposure in India

Quantifying nitrous oxide emissions in the U.S. Midwest: a top‐down study using high resolution airborne in‐situ observations

The densely farmed U.S. Midwest is a prominent source of nitrous oxide (N2O) but top‐down and bottom‐up N2O emission estimates differ significantly. We quantify Midwest N2O emissions by combining observations from the Atmospheric Carbon and Transport‐America campaign with model simulations to scale the Emissions Database for Global Atmospheric Research (EDGAR). In October 2017 we scaled agricultural EDGAR v4.3.2 and v5.0 emissions by factors of 6.3 and 3.5, respectively, resulting in 0.42 nmol m−2 s−1 Midwest N2O emissions. In June/July 2019, a period when extreme flooding was occurring in the Midwest, agricultural scaling factors were 11.4 (v4.3.2) and 9.9 (v5.0), resulting in 1.06 nmol m−2 s−1 Midwest emissions. Uncertainties are on the order of 50 %. Agricultural emissions estimated with the process‐based model DayCent (Daily version of the CENTURY ecosystem model) were larger than in EDGAR but still substantially smaller than our estimates. The complexity of N2O emissions demands further studies to fully characterize Midwest emissions