[Air Quality Forecasting] - PowerPoint Presentation

Slide1

[Air Quality Forecasting]

Breakout Report

1

st

TEMPO Applications Workshop

Huntsville, ALSlide2

General Comments

First time a suite of ozone pre-cursors, ozone, PM2.5 will be measured on hourly time scale.  Lends itself to investigating photochemical processes leading to ozone and PM2.5 and see how this knowledge can be helped to improve model chemistry and thus predictions.

TEMPO high spatial and temporal resolution will be valuable for regions of complex meteorological flows (

ie

mountain/valley flows, lake/sea breeze circulations)

TEMPO free

tropospheric

ozone retrievals will be valuable for constraining background ozone, intercontinental transport and stratospheric intrusion events

TEMPO NO2 (and potentially H2CO) columns will be valuable for constraining emissions and surface ozone predictions

GOES-R/TEMPO synergies will be critical for wildfire, lightning

NOx

,

VolAsh

, aerosol/cloud interaction forecasting

Lightning imager flash

energetics

can be used to get

NOx

emissions estimates: use Gulf of Mexico observations to compare TEMPO

NOx

column to GLM

NOx

emissions

Coupling between aerosols, deposition and coastal ocean color should be expanded. TEMPO has wavelengths for ocean color retrieval but lacks wavelengths for atmospheric correction.

Intercomparison

between TEMPO and GOES-R aerosol product s will be valuable for understanding information content of different retrievals (useful for data assimilation constraints)

Recommendations:

Use GEOTASO data as TEMPO proxy to demonstrate real applications of TEMPO data (

ie

, emission constraints, improved ozone/aerosol forecast) – Needs advancement in GEOTASO retrieval capability

Continued efforts to

reacognize

that to fully utilize TEMPO data and the GEO Constellation we need to maintain a global perspective (including the modeling elements)

Integrate TEMPO products into web forecasting tools such as

eIDEA

and visualization and data access tools such as World View

Need to develop tools (

ie

RSIG, WHIPS) to conduct model evaluation using

TEMPO dataSlide3

Question 1: TEMPO

will provide first of its kind temporal (1 hourly) and spatial (2-km pixel width in north-south; 4.5-km pixel length in east-west) resolution with limited data latencies allowing for the community to continue existing applications and facilitate new ones. What are the discipline or focus area science questions or application challenges that can be addressed with TEMPO data?

Improved characterization of background ozone for regional AQ prediction

Improved constraints on

NOx

emissions and absorbing AOD and aerosol heights will improve forecasts of O3 production in fire plumes Slide4

Question 2: There are a number of current in situ and remotely sensed data that measure atmospheric constituents, such as tropospheric ozone (O

3

), nitrogen dioxide (NO

2), aerosols, and other trace pollutants (formaldehyde [H

2

CO], glyoxal [C2H2O2], and sulfur dioxide [SO2]). Which instruments/observations do you currently use for your work?

AIRNow

,

Aeronet

, OMI (Total ozone, absorbing AOD,

Tropospheric

NO2 column, SO2 column, trend analysis for emission adjustment), MODIS VIIRS, GOES, AHI AOD, CALIPSO/CATS aerosol extinction for validation, MISR, AIRS O3, CO Slide5

Question 3:

A variety of current and future partners exist in the community. Who are your key partners or end user organizations on tasks, projects, or processes that use NASA satellite data? Who are additional potential users

?

NWS: State and local AQ forecasters, global scale modeling

ARL: Washington and Alaska VAAC, smoke forecasts

Washington State: Pacific Northwest, US Forest Service Georgia Tech: SE States, Federal and State Forest ServiceU-Iowa: Support for Airborne campaignsSlide6

Question 4:

Given what you have learned about TEMPO over the last two days, what additional or higher level data products from TEMPO might be useful in your science or applications tasks? (Include the characteristics of the product and other requirements, e.g. resolution, accuracy, data latency, data format.) Rank them in importance

.

Quantification of uncertainty, detection limits, and any data aggregation or missing values

Availability of a priori (and their

obs error covariances were applicable) as well as sufficiently detailed information on retrievals and removal of stratospheric components

Quality flags, observation error std. dev., observation error correlation matrix where appropriate (e.g. for ozone profile, averaging kernels were appropriate)

Cloud info and some info on expect impact from clouds if some data provided for partly cloudy conditions.

Regional sub setting capability to limit data volumeSlide7

Question 5: Within your organization, what are the biggest impediments limiting your use of new satellite data and products?

NWS: Latency (require 2 hour latency for operational forecasting, unless 24hr analysis cycle is performed prior to forecast, older forecast)

Washington State: Limited temporal sampling due to polar orbit,

Uncertianty

estimates and retrieval sensitivity, lack of aerosol speciation

U-Iowa: Retrieval artifacts (cloud clearing, surface reflectance, land/ocean biases) Slide8

Question 6:

What data formats (e.g.,

NetCDF

, HDF, GIS-compatible, etc.) do you need for your science or applications tasks?

GIS layer files (e.g.,

ArcGIS) for derived pollutant surfaces; kml for Google EarthBUFRASCII or NetCDF files available in real-time on an FTP site for automated download, analysis and alertingMake data available by

latitude,longitude

AND on a grid. Trying to extract data at specific lat-long points is a major pain for a lot of met data format