GAW/GURME pilot studies and experience with AQ modelling and forecasting

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1 GAW URBAN RESEARCH METEOROLOGY & ENVIRONMENT PROJECT (GURME) GAW/GURME pilot studies and experience with AQ modelling and forecasting Ranjeet S Sokhi Centre for Atmospheric and Instrumentation Research (CAIR) University of Hertfordshire, UK

2 GURME Science Advisory Group Veronique Bouchet (Chair) Environ and Climate Change Canada, Montreal, Canada Luisa Molina (co-chair) Molina Center for Energy and the Environment, USA Gufran Beig - Indian Institute of Tropical Meteorology, India Sue Grimmond University of Reading, UK Jhoon Kim Yonsei University, Seoul, Korea Pablo Saide Chile (currently at NCAR, Boulder, USA) Paulo Saldiva University of São Paulo, Brazil Heinke Schuenzen University of Hamburg, Germany Ranjeet Sokhi - University of Hertfordshire, UK Acknowledgements Jianguo Tan Chinese Meteorological Administration, Shanghai, China WMO - Alexander Baklanov WMO Secretariat and Oksana Tarasova Chief, WMO/GAW Research Acknowledgements to Liisa Jalkanen, Greg Carmichael and all past and current GURME SAG members

3 GAW Urban Research Meteorology and Environment Project (GURME) Created under GAW in 1995: To enhance the capabilities of NMHSs in providing urban-environmental forecasting and air quality services of high quality, illustrating the linkages between meteorology and air quality; 1 st AQ Forecasting System in India: Forecasting Air Quality of tomorrow SAFAR (G. Beig, IITM) Shanghai Expo: multi-hazards forecast system (Lead by Tang Xu, SMB) 3 Latin American cities Signature of MOU between Chilean Meteorological Office and UNAB to transfer AQ forecasting model to the Met Office.

4 A RENEWED ORIENTATION FOR GURME WMO 17 Congress (May 2015): Congress stressed that WMO and its Members can make a tangible positive impact on the urban environment by providing forecasts and integrated services that are targeted to the wide-ranging needs of urban authorities and population In parallel, new SAG on APPlications created with focus on global and regional modelling and analyses. GURME centers its focus on models and applications appropriate to describe the urban environment and how these environments interact with the regional and global scales. Activities ultimately aim at mitigating hazards that have direct impacts on health.

5 GURME highlights Research activities Research activities are aimed at: o Lowering the barriers to carry out modelling at increasing resolutions and the urban context o Addressing identified gaps especially where the research questions transcends disciplines and requires leveraging a broader community o Promoting seamless modelling approaches all the way to urban systems Release of the PanAm2015 Legacy Database as a test bed Funded by NERC-MOES, PROMOTE aims to reduce uncertainties in air quality prediction and forecasting for Delhi through process analyses and linking air pollutant concentrations and emission controls Initiating review on urban surface characteristics accessibility and use in high resolution modelling Participating in and sponsoring expert meetings: IWAQFR, CCM, WWOSC

6 GURME highlights Demonstrations and R2O Demonstrations: Santiago, Mexico, Pune/Mumbai/New Delhi, Shanghai R2O Pilots coordinated by GURME have often led to AQ forecasting systems that continue to be operated after the R&D phase GURME currently advises on expansion, further improvements and downscaling to better represent the urban environment Collaborate explicitly with APP-SAG on nesting from global to urban & associated initiatives NA NA-1 SA-1 SA-3 SA-2 SA EU WA WA-1 EU-1 ME ME-1 IA IA-1 IA-2 Image from NASA - U.S. Geological Survey Demonstrations: Approaching new locations in more holistic fashion as GURME is where Urban Meteorology is specifically identified R2O: - Pilots may involve a broader scope compared to existing ones more focussed on AQ - Working actively to foster joint work with other WMO programs/ WGs / SAGs

7 Examples of WMO-GURME Highlight Projects Chile Mexico UK India SAFAR Delhi - PROMOTE

8 Forecasting for severe PM2.5 episodes in south - central Chile. Source: Pablo Saide (NCAR) Produced by a combination of: Complex topography Episodic meteorological conditions Emissions due to anthropogenic activities Episodes are declared to warn the public and to try to reduce the impact by invoking temporary measures Santiago during an episode Ref: Saide et al. (JGR 2016) % reduction in Pre-emergencyand emergencies from 2013 to May 1 to August Alertas Preemergencias Emergencias

9 GURME Pilot Project Urban Health, Air Quality and Climate: Mexico City Case Study Source: Luisa Tan Molina Based on the MOU signed between WMO and Mexico City Environment Secretariat (Secretaria de Medio Ambiente or SEDEMA) and information provided by SEDEMA

10 GURME Pilot Project: Urban Health, Air Quality and Climate: Mexico City Case Study Objectives By forecasting AQ conditions on short timescales, support the Mexico City government with information to take effective actions through mitigation to population exposure of high pollutants concentrations and lay the groundwork to develop policies to reduce pollutants emissions for air quality improvement and other co-benefits (e.g., climate, food security, etc.) Identify synergies with existing activities and create synergies with other initiatives (health, air quality and environment, development) of Mexico City s government Capacity building learning by doing Raising awareness of the health benefits of emissions reductions Expand model coverage, replicating in other cities and megacities, especially in developing countries.

11 Activities currently underway or being developed by CDMX Environment Secretariat Continuous strengthening of the air quality monitoring network Auditing of the Mexico City Metropolitan Area emissions inventory Follow up and assessment of the Air Quality Management Program (PROAIRE) local competency actions Carrying out the historical health improvement assessment due to emissions reductions from 1990 to 2012 together with Harvard School of Public Health Developing the WRF-CMAQ parametrization for the specific case of Mexico City s megalopolis together with the Barcelona Supercomputing Center (BSC) Analyzing the possibility of measuring air pollution concentrations at street level to compare with background concentrations in order to assess health consequences of exposure to high pollutants levels in this microenvironment.

12 Air quality forecasting system for Mexico City PRONÓSTICO METEOROLÓGICO IC/B C nestin g nestin g nestin g NCEP GFS 0.25º x 0.25º WRF-ARW 27 km x 27 km WRF-ARW 9 km x 9 km WRF-ARW 3 km x 3 km WRF-ARW 1 km x 1 km PRONÓSTICO DE CALIDAD DEL AIRE Meteo Mete o PRONÓSTICO EMISIONES BC nestin g Emi s UCAR MOZART4 1.9 º x 2.5º CMAQ 3 km x 3 km CMAQ 1 km x 1 km HERMES-Mex 1 km x 1 km HERMES-Mex 3 km x 3 km Post-procesos PRODUCTOS

13 AN EARLY WARNING SYSTEM FOR AIR QUALITY ISO 9001:2008 Accredited by Standard Certification Council-India World Meteorological Organization (United Nations) recognized GUFRAN BEIG INDIAN INSTITUTE OF TROPICAL METEOROLOGY, PUNE ESSO, Ministry of Earth Sciences, Govt. of India

14 Extreme air pollution event challenge 14 of sudden changing toxicity Fine dust transporting from Gulf region Diwali = f [weather (Oct-Nov)]

15 What is needed for Forecasting Systems OBSERVATIONAL NETWORK Monitor Air Quality & Weather Parameters all over City PM10, PM2.5, PM1, O3, CO, NOx(NO+NO2), CH4, VOCs, SO2 Rainfall, Temperature, Humidity, Wind speed, Wind direction, UV Radiation EMISSION INVENTORIES Accounting sources of pollution Identify air pollution hot spots over city, crucial input for forecasting model FORECASTING MODEL Develop Technology to Predict AQ Develop capacity to predict air quality 1 to 3 days in advance by advance computing OUTREACH Convert science in to Information Spread Awareness, Educate Public and Provide Advisory Service with AQI based Precautionary Statement

16 SAFAR -Early Warning System Components System of Air quality and weather Forecasting and Research (SAFAR)- Ahmedabad 1-3 days Advance Forecast for Air Quality & Weather Parameters PM 1, PM 2.5, PM 10, O 3, NO X, CO, SO 2, BC, Hg, BTX, T, H, P, WS, WD, Rain, UV SAFAR-SCIENTIFIC COMPONENTS Air Quality and Weather observation network Emission inventory development -10 AQMS -15 AWS SAFAR-PRODUCTS FOR SOCIETY Air Quality Now & 3D Forecast Heath Advisory and Precaution s UV Radiation@ Skin Advisories SAFAR-SERVICES FOR SOCIETY Web portal Mobile App Air Quality and Weather Forecasting Translating Data to Information Resoluti on -1x1km 2 72 hr lead forecast 1.6x1.6k m 2 AQI & Advisori es Weather Now & 3D TEMPERATURE, Forecast HUMIDITY, WIND SPEED, WIND DIRECTION, SOLAR RADIATION, RAINFALL Extreme Event Alerts City Emission Maps LED Display Boards IVRS Service

17 Hotspots and Sources for NCT-DELHI Emission Hot Spots: Okhala Industrial area, Tuglakabad, Badarpur, ITO junction of indraprast marg, Shivaji park, Punjabi bag

18 Hotspots and Sources for NCT-DELHI Emission Hot Spots: Okhala Industrial area, Tuglakabad, Badarpur, ITO junction of indraprast marg, Shivaji park, Punjabi bag

19 PROMOTE Process analysis, observations and modelling Integrated solutions for cleaner air for Delhi UK Teams: Hertfordshire - Sokhi, Rohekar, Tiwari, Webber, Chemel Manchester - McFiggans, Coe Cambridge - Archibald Leeds - Chipperfield and Carslaw Lancaster - Wild Edinburgh - Doherty CERC Carruthers, Stocker UKMO - O Connor International collaborators USEPA - Mathur NOAA Grell PNNL - Zaveri WMO/GURME Baklanov, Tarasova Indian Teams: IITM (Pune) Beig IMD (Delhi) - Peshin NARL (Tirupati) - Singh IRADe (Delhi) - Parikh IIT (Madras) - Gunthe APHH Programme funded by: NERC MoES DBT MRC Newton-Bhabha Fund

20 Multicomponent observational analysis Duration of project: Four years Lodhi Road Air pollutants: NO 2, NO x, PM 2.5, PM 10, O 3 and SO 2 Analysis years: Current (2011, 2016), 2030 (emission scenarios), 2050 (climate scenarios) Campaigns: Aerosol and BL/Fog campaigns over summer and winter Winter: (fog/primary): Dec Jan 2018 Summer (photochem/dust): May 2018 Ayanagar PROMOTE Sites: Lodhi Road, Ayanagar, other locations Parameters PM, PN, Speciated, Gaseous, Meteorological Instruments a large array of aerosol and gaseous species and meteorological measurements including satellite observations

21 High resolution multiscale air quality predictions Typical resolutions: 1 o > 50km > 10 km > 2 km > street Global Regional Urban Local Global scale: UKCA (Chemistry-Climate) GEOS-Chem (emissions scenario analysis) City to regional scales: WRF-Chem (SAFAR) WRF-CMAQ (comparator model) Street to urban scales: ADMS, OSCAR Causes of air pollution episodes London case A25: unspecified anthropogenic mass EC: Elemental carbon ORGP: Primary organic mass NH4: ammonium NO3: Nitrate SO4: Sulfate PM2.5=SO4+NO3+NH4+ORG P+EC+A25

22 Lessons learnt for success Full engagement of key partners is essential at all stages e.g. city authority, science and data partners and other stakeholders Clear local relevant questions need to be identified at the start e.g. to decide the purpose of the AQ and meteorological forecasting and prediction and how the outputs will be used to improve the life of the citizens Identify wider synergies e.g. health, air quality and environment, development Develop local competences and consider gaps in local infrastructure/data and/or develop a AQF service provision plan Main components of a comprehensive AQ modelling and forecasting capability require: appropriate quality controlled observational networks (AQ, meteorological and demographical) robust source/emission inventories and activity datasets, fit for purpose modelling approach, dissemination and outreach strategy, and translation of outputs to practical and effective mitigation/control options

23 Scientific considerations To achieve seamless forecasting, higher resolutions are required for local and urban scales in a multiscale modelling framework e.g. consistency of land cover, emissions and meteorological processing Consideration should be given to how research developments can be translated into an operational framework to benefit African countries e.g. improving the representation of urban features where data may be limited Air quality and weather forecasting needs to be considered through linkages between the urban environment in a regional and global context Similarly, changes air quality and weather needs to be considered along with changes in future climate e.g. changes in the occurrence of extreme events Local scale, short term measures can be effective but longer term improvement e.g. in health outcomes requires a more regional response