William Lahoz

Data assimilation: making sense of Earth Observation

Climate change, air quality and environmental degradation are important societal challenges for the 21st Century. These challenges require an intelligent response from society, which in turn requires access to information about the Earth System. This information comes from observations and prior knowledge, the latter typically embodied in a model describing relationships between variables of the Earth System. Data assimilation provides an objective methodology to combine observational and model information to provide an estimate of the most likely state and its uncertainty for the whole Earth System. This approach adds value to the observations – by filling in the spatio-temporal gaps in observations; and to the model – by constraining it with the observations. In this review paper we motivate data assimilation as a methodology to fill in the gaps in observational information; illustrate the data assimilation approach with examples that span a broad range of features of the Earth System (atmosphere, including chemistry; ocean; land surface); and discuss the outlook for data assimilation, including the novel application of data assimilation ideas to observational information obtained using Citizen Science. Ultimately, a strong motivation of data assimilation is the many benefits it provides to users. These include: providing the initial state for weather and air quality forecasts; providing analyses and reanalyses for studying the Earth System; evaluating observations, instruments and models; assessing the relative value of elements of the Global Observing System (GOS); and assessing the added value of future additions to the GOS.

Monitoring air quality from space: The case for the geostationary platform

Air quality (AQ) is defined by the atmospheric composition of gases and particulates near the Earths surface. This composition depends on local emissions of pollutants, chemistry, and transport processes; it is highly variable in space and time. Key lower-tropospheric pollutants include ozone, aerosols, and the ozone precursors NOx and volatile organic compounds. Information on the transport of pollutants is provided by carbon monoxide measurements. Air quality impacts human society, because high concentrations of pollutants can have adverse effects on human health; health costs attributable to AQ are high. The ability to monitor, forecast, and manage AQ is thus crucial for human society. In this paper we identify the observational requirements needed to undertake this task, discuss the advantages of the geostationary platform for monitoring AQ from space, and indicate important challenges to overcome. We present planned geostationary missions to monitor AQ in Europe, the United States, and Asia, and advocate for the usefulness of such a constellation in addition to the current global observing system of tropospheric compo

Observing System Simulation Experiments

Observing System Simulation Experiments (OSSEs) are typically designed to use data assimilation ideas (see chapter Mathematical Concepts in Data Assimilation, Nichols) to investigate the potential impacts of prospective observing systems (observation types and deployments). They may also be used to investigate current observational and data assimilation systems by testing the impact of new observations on them. The information obtained from OSSEs is generally difficult, or in some contexts impossible, to obtain in any other way.

Evaluation of satellite soil moisture products over Norway using ground-based observations

Abstract In this study we evaluate satellite soil moisture products from the advanced SCATterometer (ASCAT) and the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) over Norway using ground-based observations from the Norwegian water resources and energy directorate. The ASCAT data are produced using the change detection approach of Wagner et al. (1999) , and the AMSR-E data are produced using the VUA-NASA algorithm ( Owe et al., 2001 , Owe et al., 2008 ). Although satellite and ground-based soil moisture data for Norway have been available for several years, hitherto, such an evaluation has not been performed. This is partly because satellite measurements of soil moisture over Norway are complicated owing to the presence of snow, ice, water bodies, orography, rocks, and a very high coastline-to-area ratio. This work extends the European areas over which satellite soil moisture is validated to the Nordic regions. Owing to the challenging conditions for soil moisture measurements over Norway, the work described in this paper provides a stringent test of the capabilities of satellite sensors to measure soil moisture remotely. We show that the satellite and in situ data agree well, with averaged correlation (R) values of 0.72 and 0.68 for ASCAT descending and ascending data vs in situ data, and 0.64 and 0.52 for AMSR-E descending and ascending data vs in situ data for the summer/autumn season (1 June–15 October), over a period of 3 years (2009–2011). This level of agreement indicates that, generally, the ASCAT and AMSR-E soil moisture products over Norway have high quality, and would be useful for various applications, including land surface monitoring, weather forecasting, hydrological modelling, and climate studies. The increasing emphasis on coupled approaches to study the earth system, including the interactions between the land surface and the atmosphere, will benefit from the availability of validated and improved soil moisture satellite datasets, including those over the Nordic regions.

Data Assimilation and Information

In this introductory chapter we provide an overview of the connection between the data assimilation methodology and the concept of information, whether embodied in observations or models. In this context, we provide a step by step introduction to the need for data assimilation, culminating in an easy to understand description of the data assimilation methodology. Schematic diagrams and simple examples form a key part of this chapter.

Mapping urban air quality in near real-time using observations from low-cost sensors and model information

The recent emergence of low-cost microsensors measuring various air pollutants has significant potential for carrying out high-resolution mapping of air quality in the urban environment. However, the data obtained by such sensors are generally less reliable than that from standard equipment and they are subject to significant data gaps in both space and time. In order to overcome this issue, we present here a data fusion method based on geostatistics that allows for merging observations of air quality from a network of low-cost sensors with spatial information from an urban-scale air quality model. The performance of the methodology is evaluated for nitrogen dioxide in Oslo, Norway, using both simulated datasets and real-world measurements from a low-cost sensor network for January 2016. The results indicate that the method is capable of producing realistic hourly concentration fields of urban nitrogen dioxide that inherit the spatial patterns from the model and adjust the prior values using the information from the sensor network. The accuracy of the data fusion method is dependent on various factors including the total number of observations, their spatial distribution, their uncertainty (both in terms of systematic biases and random errors), as well as the ability of the model to provide realistic spatial patterns of urban air pollution. A validation against official data from air quality monitoring stations equipped with reference instrumentation indicates that the data fusion method is capable of reproducing city-wide averaged official values with an R2 of 0.89 and a root mean squared error of 14.3 μg m-3. It is further capable of reproducing the typical daily cycles of nitrogen dioxide. Overall, the results indicate that the method provides a robust way of extracting useful information from uncertain sensor data using only a time-invariant model dataset and the knowledge contained within an entire sensor network.

A Network of Low-Cost Air Quality Sensors and Its Use for Mapping Urban Air Quality

Recent rapid technological advances in sensor technology have resulted in a wide variety of small and low-cost microsensors with significant potential for measuring air pollutants. In this contribution, we evaluate the performance of a commercially available low-cost sensor platform for air quality and show how the data from a network of such devices can be used for high-resolution mapping of urban air quality. Our results indicate that the sensor platforms are subject to a significant sensor-to-sensor variability as well as strong dependencies on environmental conditions. A field calibration of all individual sensor devices by co-locating them with an air quality monitoring station equipped with reference instrumentation is thus required for obtaining the best possible results. We further demonstrate that, despite relatively low accuracy at the individual sensor level, a methodology based on geostatistical data fusion is capable of merging the information from the sensor network with model information in such a way that we can obtain realistic and frequently updated maps of urban air quality. We show that exploiting the “swarm knowledge” of the entire network of sensors is capable of extracting useful information from the data even though individual sensors are subject to significant uncertainty.

Observing System Simulation Experiments (OSSEs) for Air Quality Applications

In the past few years a growing amount of space observations focusing on atmospheric composition has become available and this trend will continue with the launch of new satellites (ESA-Sentinels, NASA-TEMPO and JAXA air quality and climate mission) in the near future. To justify the production and launch of these expensive instruments, there is a need for determining the added value of future satellite instruments and their optimal design in an objective way. One methodology that can do so is the OSSE (Observing System Simulation Experiment). Although extensively used in the meteorological community, it’s use in the field of air quality and climate is still limited and a common approach is desirable. Based on existing studies and experience in the meteorological community we have identified requirements for each of the OSSE elements for performing a realistic OSSE. Using illustrative examples from existing air quality OSSEs we will present the methodology and the requirements for the application of OSSEs to satellite observations of atmospheric composition.

General Concepts in Meteorology and Dynamics

The aim of this chapter is to give a general overview of the atmospheric circulation, highlighting the main concepts that are important for a basic understanding of meteorology and atmospheric dynamics relevant to atmospheric data assimilation.