We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.
This study examines the benefit of using a dynamical ensemble for 48 hr deterministic and probabilistic predictions of near‐surface fine particulate matter (PM2.5) over the contiguous United States (CONUS). Our ensemble design captures three key sources of uncertainties in PM2.5modeling including meteorology, emissions, and secondary organic aerosol (SOA) formation. Twenty‐four ensemble members were simulated using the Community Multiscale Air Quality (CMAQ) model during January, April, July, and October 2016. The raw ensemble mean performed better than most of the ensemble members but underestimated the observed PM2.5over the CONUS with the largest underestimation over the western CONUS owing to negative PM2.5bias in nearly all the members. To improve the ensemble performance, we calibrated the raw ensemble using model output statistics (MOS) and variance deficit methods. The calibrated ensemble captured the diurnal and day‐to‐day variability in observed PM2.5very well and exhibited almost zero mean bias. The mean bias in the calibrated ensemble was reduced by 90–100% in the western CONUS and by 40–100% in other parts of the CONUS, compared to the raw ensemble in all months. The corresponding reduction in root‐mean‐square error (RMSE) was 13–40%. The calibrated ensemble also showed 30% improvement in the RMSE and spread matching compared to the raw ensemble. We have also shown that a nine‐member ensemble based on combinations of three meteorological and three anthropogenic emission scenarios can be used as a smaller subset of the full ensemble when sufficient computational resources are not available in the operational setting.
more » « less- PAR ID:
- 10378963
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 125
- Issue:
- 16
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
more » « less
Abstract Previous research on the health and air quality impacts of wildfire smoke has largely focused on the impact of smoke on outdoor air quality; however, many people spend a majority of their time indoors. The quality of indoor air on smoke-impacted days is largely unknown. In this analysis, we use publicly available data from an existing large network of low-cost indoor and outdoor fine particulate matter (PM2.5) monitors to quantify the relationship between indoor and outdoor particulate air quality on smoke-impacted days in 2020 across the western United States (US). We also investigate possible regional and socioeconomic trends in this relationship for regions surrounding six major cities in the western US. We find indoor PM2.5concentrations are 82% or 4.2
µ g m−3(median across all western US indoor monitors for the year 2020; interquartile range, IQR: 2.0–7.2µ g m−3) higher on smoke-impacted days compared to smoke-free days. Indoor/outdoor PM2.5ratios show variability by region, particularly on smoke-free days. However, we find the ratio of indoor/outdoor PM2.5is less than 1 (i.e. indoor concentrations lower than outdoor) at nearly all indoor-outdoor monitor pairs on smoke-impacted days. Although typically lower than outdoor concentrations on smoke-impacted days, we find that on heavily smoke-impacted days (outdoor PM2.5> 55µ g m−3), indoor PM2.5concentrations can exceed the 35µ g m−324 h outdoor standard set by the US Environmental Protection Agency. Further, total daily-mean indoor PM2.5concentrations increase by 2.1µ g m−3with every 10µ g m−3increase in daily-mean outdoor PM2.5.(median of statistically significant linear regression slopes across all western US monitor pairs; IQR: 1.0–4.3µ g m−3) on smoke-impacted days. These results show that for indoor environments in the western US included in our analysis, remaining indoors during smoke events is currently an effective, but limited, strategy to reduce PM2.5exposure. -
more » « less
Abstract This paper examines the accuracy of Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) generated 72 hr fine particulate matter (PM2.5) forecasts in Delhi during the crop residue burning season of October‐November 2017 with respect to assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals, persistent fire emission assumption, and aerosol‐radiation interactions. The assimilation significantly pushes the model AOD and PM2.5toward the observations with the largest changes below 5 km altitude in the fire source regions (northeastern Pakistan, Punjab, and Haryana) as well as the receptor New Delhi. WRF‐Chem forecast with MODIS AOD assimilation, aerosol‐radiation feedback turned on, and real‐time fire emissions reduce the mean bias by 88–195 μg/m3(70–86%) with the largest improvement during the peak air pollution episode of 6–13 November 2017. Aerosol‐radiation feedback contributes ~21%, ~25%, and ~24% to reduction in mean bias of the first, second, and third days of PM2.5forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM2.5forecasts driven by persistent fire emissions was only 6% lower compared to those driven by real fire emissions. Aerosol‐radiation feedback extends the benefits of assimilating satellite AOD beyond PM2.5forecasts to surface temperature forecast with a reduction in the mean bias of 0.9–1.5°C (17–30%). These results demonstrate that air quality forecasting can benefit substantially from satellite AOD observations particularly in developing countries that lack resources to rapidly build dense air quality monitoring networks.
-
more » « less
Abstract Increasing fire activity and the associated degradation in air quality in the United States has been indirectly linked to human activity via climate change. In addition, direct attribution of fires to human activities may provide opportunities for near term smoke mitigation by focusing policy, management, and funding efforts on particular ignition sources. We analyze how fires associated with human ignitions (agricultural fires and human-initiated wildfires) impact fire particulate matter under 2.5
µ m (PM2.5) concentrations in the contiguous United States (CONUS) from 2003 to 2018. We find that these agricultural and human-initiated wildfires dominate fire PM2.5in both a high fire and human ignition year (2018) and low fire and human ignition year (2003). Smoke from these human levers also makes meaningful contributions to total PM2.5(∼5%–10% in 2003 and 2018). Across CONUS, these two human ignition processes account for more than 80% of the population-weighted exposure and premature deaths associated with fire PM2.5. These findings indicate that a large portion of the smoke exposure and impacts in CONUS are from fires ignited by human activities with large mitigation potential that could be the focus of future management choices and policymaking. -
more » « less
Abstract This study analyzes the low short-range predictability of the 3 May 2020 derecho using a 40-member convection-allowing Model for Prediction Across Scales (MPAS) ensemble. Elevated storms formed in south-central Kansas late at night and evolved into a progressive mesoscale convective system (MCS) during the morning while moving across southern Missouri and northern Arkansas, and affected western and middle Tennessee and southern Kentucky in the afternoon. The convective initiation (CI) in south-central Kansas, the organization of a dominant bow echo MCS, and the MCS maintenance over Tennessee were identified as the three main predictability issues. These issues were explored using three MPAS ensemble members, observations, and the Rapid Refresh analyses. The MPAS members were classified as successful or unsuccessful with regard to each predictability issue. CI in south-central Kansas was sensitive to the temperature and dewpoint profiles in low levels, which were associated with greater elevated thermodynamic instability and lower level of free convection in the successful member. The subsequent organization of a dominant bowing MCS was well predicted by the member that had more widespread convection in the early stages and no detrimental interaction with other simulated convective systems. Last, the inability of MPAS ensemble members to predict the MCS maintenance over western and middle Tennessee was linked to a dry bias in low levels and much lower thermodynamic instability ahead of the MCS compared to observations. This case demonstrates the challenges in operational forecasting of warm-season derecho-producing progressive MCSs, particularly when ensemble numerical weather prediction guidance solutions differ considerably.
