This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640276.
G2.07 Lack of uptake of lidar measurements in data assimilation
Submitted by Anna Mikalsen on 9 June, 2017 - 16:10
Gap abstract:
Aerosol lidar data can potentially be used to constrain uncertain model processes in global aerosol-climate models. Satellite-borne lidar data can be effectively assimilated to improve model skill but, currently, aerosol lidar data assimilation experiments are mainly limited to the assimilation of attenuated backscatter, which is a non-quantitative optical property of aerosol. There is much additional valuable data that could be utilised to improve data assimilation. Such improved data assimilation may allow attenuation of data to allow broader inferences about satellite quality as being developed by GAIA-CLIM for temperature and humidity via the GRUAN processor.
Part I Gap description
Primary gap type:
- Knowledge of uncertainty budget and calibration
ECVs impacted:
- Aerosols
User category/Application area impacted:
- Operational services and service development (meteorological services, environmental services, Copernicus Climate Change Service (C3S) and Atmospheric Monitoring Service (CAMS), operational data assimilation development, etc.)
- International (collaborative) frameworks and bodies (space agencies, EU institutions, WMO programmes/frameworks etc.)
Non-satellite instrument techniques involved:
- Lidar
Detailed description:
Uncertainties associated with aerosol emissions in terms of their intensity and distribution pattern, atmospheric processes, and optical properties, represent a significant part of the uncertainty associated with the quantification of the impact of aerosols on climate and air quality in regional and global models. Lidar assimilation in global aerosol-climate models is an active area of research at many forecasting centres and research institutes. Assimilation systems used range from variational to ensemble methods, variables assimilated are aerosol extinction and backscatter coefficients or lidar raw signals (by using customized forward models). Applications range from aerosol global forecasts, to volcanic ash detection and regional air quality.
Data assimilation techniques are implemented to decrease these uncertainties, constraining models with available information from observations in order to make a best estimate of the state of the atmosphere. The short-range forecasts from such systems have the potential to be useful for the calibration/validation (Cal/Val) of new satellite data as they provide a stable reference for inter-comparison between products from different satellites. In particular, the use of a forecast model minimises errors due to temporal differences when comparing two different observational datasets.
This Cal/Val technique has been found to be useful for satellite observations sensitive to temperature and humidity, since the short-range forecasts are highly accurate for these variables, and this has been explored further within the GAIA-CLIM project. However, for aerosol products the short-range forecasts are not yet accurate enough to be able to identify more than gross errors in the satellite observations.
Further improvements to the aerosol data assimilation systems are needed, particularly in the area of bias correction, before aerosol forecasts can be used as a reference for satellite Cal/Val. This is a long-term goal, however, and in the short-term direct comparisons between aerosol observations should continue to be carried out for the Cal/Val of new satellite products.
Aerosol lidar data can also be used to constrain uncertain model processes in global aerosol-climate models. Satellite-borne lidar data can be effectively assimilated to improve model skill but, currently, aerosol lidar data assimilation experiments are mainly involving lidar attenuated backscatter, which is a non-quantitative optical property of aerosol. Ground based lidar networks can in addition provide quantitative measurements of aerosol backscatter and extinction coefficients. However, a limited number of aerosol lidar data assimilation experiments have been performed, preventing us from assessing the effective impact of assimilating continuous satellite lidar data and whether the current state of the lidar technology fulfils the modellers’ needs.
Operational space missions or space instruments impacted:
- Copernicus Sentinel 4/5
- Active sensors
Validation aspects addressed:
- Assimilated product (Level 4)
Gap status after GAIA-CLIM:
- After GAIA-CLIM this gap remains unaddressed
GAIA-CLIM has undertaken no specific activities to help addressing this gap.
Part II Benefits to resolution and risks to non-resolution
| Identified benefit | User category/Application area benefitted | Probability of benefit being realised | Impacts |
|---|---|---|---|
Improved model performances to determine aerosol effect at a global scale on weather and climate |
|
| Reduction of the IPCC identified uncertainties related to the aerosol direct and indirect effects, with a consequent improvement of climate and weather forecast. |
| Identified risk | User category/Application area at risk | Probability of risk being realised | Impacts |
|---|---|---|---|
Bias correction for satellite lidar data using a variational bias correction scheme not feasible |
|
| Assimilation of satellite lidar data will continue to bias the model output instead of improving the forecast skills. |
Larger uncertainty if aerosol lidar data are not used to constrain uncertain model processes in global aerosol-climate models. |
|
| Uncertainties associated with aerosol emissions impacts on climate and air quality simulations in regional and global models. |
Part III Gap remedies
Gap remedies:
Remedy 1: Extension of the GAIA-CLIM data assimilation approach to aerosol lidars
Primary gap remedy type:
Research
Proposed remedy description:
New solutions for assessing and enhancing the value of lidar data assimilation must be developed. This requires efforts in two complementary areas:
- Aerosol lidar networks must strongly work on their capability to provide NRT data, through the implementation of automatic processing calculus chains and to adopt shared/common metadata international standards in order to facilitate the data usage and manipulation.
- Modellers must develop methodologies to use the available lidar Near-Real time (NRT) data for routine evaluation of operational models or data assimilation, through the development of improved forward operators, while quality-checked (QC) and added-value (higher level data) products must be used for the retrospective assessments of model simulations (reanalysis/reforecast).
Building on the growing interest from the global NWP community in using high accuracy data from ground-based networks to constrain satellite data biases, ground-based lidar data could be used by modellers also to anchor the bias correction for satellite lidar data, using a variational bias correction scheme.
However, further work must be implemented aimed at improving model skill, i.e models are better at predicting horizontal transport than vertical distributions. Formulation of a specified work plan should take into account that:
- Collaboration with data providers is paramount;
- NRT data delivery from all lidar satellite missions is important;
- With respect to other lidar measurements of atmospheric composition, the community is largely ready to use lidar data to improve aerosol predictions;
- Wind data will also improve atmospheric composition prediction by improving the model wind fields.
Relevance:
Aerosol is one the key factors in the determination of the radiative balance with its direct and indirect effect. An appropriate and successful assimilation within numerical models may strongly improve our climate knowledge as well as the prediction of severe weather events. This value is enhanced by the multitude of data which will be available at global scale – with the advent of upcoming satellite missions with a lidar technique on-board including the ESA missions, ADM-Aeolus and EarthCARE.
Measurable outcome of success:
A number of initiatives are currently ongoing and their outcome will give us within a few years a quantitative idea of the importance of using lidar measurements in data assimilation
Expected viability for the outcome of success:
- High
Scale of work:
- Consortium
Time bound to remedy:
- Less than 5 years
Indicative cost estimate (investment):
- High cost (> 5 million)
Indicative cost estimate (exploitation):
- Yes
Potential actors:
- National Meteorological Services
- Academia, individual research institutes