H. Järvinen

Doppler radar wind data assimilation with HIRLAM 3DVAR

A Doppler radar wind data assimilation system has been developed for the three-dimensional variational data assimilation (3DVAR) scheme of the High Resolution Limited Area Model (HIRLAM). Radar wind observations can be input for the multivariate HIRLAM 3DVAR either as radial wind superobservations (SOs) or as vertical profiles of horizontal wind obtained with the velocity‐azimuth display (VAD) technique. The radar wind data handling system, including data processing, quality control, and observation operators for the 3DVAR, are described and evaluated. Background error standard deviation (sb) in observation space for wind and radial wind have been estimated by the so-called randomization method. The derived values of sb are used in the quality control of observations and also in the assignment of radar wind observation error standard deviations (so). Parallel data assimilation and forecast experiments confirm reasonably tuned error statistics and indicate a small positive impact of radar wind data on the verification scores, for both inputs.

Assimilation of radar radial winds in the HIRLAM 3D-var

During the last decade several attempts of assimilating radar wind data into atmospheric models have been reported by various research groups. Some of these are briefly reviewed here. A three-dimensional variational data assimilation (3D-Var) scheme for the High Resolution Limited Area Model (HIRLAM) forecasting system has been developed and prepared for assimilation of low elevation angle radar radial wind superobservations. The HIRLAM 3D-Var is based on a minimization of a cost function that consists of one term measuring the distance between the resulting analysis and a background field, which is a short-range forecast, and another term measuring the distance between the analysis and the observations. The development required for assimilating the radial wind data includes software for generating and managing the superobservations from polar volume data, a quality control algorithm and an observation operator for providing the model counterpart of the observation. The functionality of the components have been evaluated through assimilation experiments using data from Finnish and Swedish radars and further studies are underway

Doppler radar radial winds in HIRLAM. Part II: optimizing the super-observation processing

Abstract Doppler radar radial wind observations are modelled in numerical weather prediction (NWP) within observation errors which consist of instrumental, modelling and representativeness errors. The systematic and random modelling errors can be reduced through a careful design of the observation operator (Part I). The impact of the random instrumental and representativeness errors can be decreased by optimizing the processing of the so-called super-observations (spatial averages of raw measurements; Part II). The super-observation processing is experimentally optimized in this article by determining the optimal resolution for the super-observations for differentNWPmodel resolutions. A 1-month experiment with the HIRLAM data assimilation and forecasting system is used for radial wind data monitoring and for generating observation minus background (OmB) differences. The OmB statistics indicate that the super-observation processing reduces the standard deviation of the radial wind speedOmBdifference, while themean vectorwindOmBdifference tends to increase. The optimal parameter settings correspond at a measurement range of 50 km (100 km) to an averaging area of 1.7 km2 (7.3 km2). In conclusion, an accurate and computationally feasible observation operator for the Doppler radar radial wind observations is developed (Part I) and a super-observation processing system is optimized (Part II).

Doppler radar radial winds in HIRLAM. Part I: observation modelling and validation

Abstract An observation operator for Doppler radar radial wind measurements is developed further in this article, based on the earlier work and considerations of the measurement characteristic. The elementary observation operator treats radar observations as point measurements at pre-processed observation heights. Here, modelling of the radar pulse volume broadening in vertical and the radar pulse path bending due to refraction is included to improve the realism of the observation modelling. The operator is implemented into the High Resolution Limited Area Model (HIRLAM) limited area numerical weather prediction (NWP) system. A data set of circa 133 000 radial wind measurements is passively monitored against theHIRLAM six-hourly background values in a 1-month experiment.No data assimilation experiments are performed at this stage. A new finding is that the improved modelling reduces the mean observation minus background (OmB) vector wind difference at ranges below 55 km, and the standard deviation of the radial wind OmB difference at ranges over 25 km. In conclusion, a more accurate and still computationally feasible observation operator is developed. The companion paper (Part II) considers optimal super-observation processing of Doppler radar radial winds for HIRLAM, with general applicability in NWP.

Improved power-law estimates from multiple samples provided by millennium climate simulations

Using the long annual mean temperature time series provided by millennium Earth System Model simulations and a method of discrete Fourier transform with varying starting point and length of time window together with averaging, we get good fits to power laws between two characteristic oscillatory timescales of the model climate: multidecadal (50–80 years) and El Nino (3–6 years) timescales. For global mean temperature, we fit β ∼ 0.35 in a relation S(f) ∼ f−β in a simulation without external climate forcing and β over 0.7 in a simulation with external forcing included. The power law is found both with and without external forcing despite the forcings, e.g. the volcanic forcing, not showing similar behaviour, indicating a nonlinear temperature response to time-varying forcing. We also fit a power law with β ∼ 8 to the narrow frequency range between El Nino frequencies (up to 1/(3.2 years)) and the Nyquist frequency (1/(2 years)). Also, monthly mean temperature time series are considered and a decent power-law fit for frequencies above 1/year is obtained. Regional variability in best-fit β is explored, and the impact of choosing the frequency range on the result is illustrated. When all resolved frequencies are used, land areas seem to have lower βs than ocean areas on average, but when fits are restricted to frequencies below 1/(6 years), this difference disappears, while regional differences still remain. Results compare well with measurements both for global mean temperature and for the central England temperature record.

Estimation of Spatial Global Positioning System Zenith Delay observation error covariance

In this paper, we present new methods and estimates of the spatial (horizontal) covariance of the ground-based Global Positioning System Zenith Delay observation errors. An algorithm is developed which enables estimation of the observation error covariance as a linear combination of innovation covariances of Zenith Total Delay (ZTD) at ground-based receiver stations, surface pressure at synoptic stations and integrated water vapour at radiosonde stations, respectively. Innovation (observation minus model background) sequences computed with the High-Resolution Limited Area Model (HIRLAM) are used as statistical material for the estimation.We present a four-parameter exponential observation error covariance model for the ZTD horizontal observation error covariance. Seasonal and yearly mean models are provided for implementation into meteorological data assimilation systems.

Effect of troposphere slant delays on regional double difference GPS processing

The demand for geodetic time series that are accurate and stable is increasing. One factor limiting their accuracy is troposphere refraction, which is hard to model and compute with sufficient resolution, both in time and space. We have studied the effect of numerical weather model (NWM)-derived troposphere slant delays and the most commonly used mapping functions, Niell and Vienna, on Global Positioning System (GPS) processing. Six months of data were calculated for a regional Finnish network, FinnRef, which consists of 13 stations, using Bernese v. 5.0 in double difference mode. The results showed that when site-specific troposphere zenith delays or gradients are not estimated, the use of NWM-based troposphere delays improved the repeatabilities of all three components of station positions (north, east and up) statistically significantly and up to 60%. The more realistic troposphere model also reduces the baseline length dependence of the solution. When site-specific troposphere delays and the horizontal gradients were estimated, there was no statistically significant improvement between the different solutions.

The Role of Error Covariances in Estimation of Aerosol Number Concentrations

The evolution of error covariance and its impact in Kalman Filtering are examined. A synthetic aerosol size distribution and an associated error covariance matrix are used as an input for a tangent-linear box model simulating aerosol microphysics. The evolution of the error correlation structures are found to be robust with respect to changes in ambient vapor conditions and nucleation schemes. The near-diagonal error correlations evolve only modestly for particles larger than ∼20–30 nm, and the nucleation and condensation processes cause strong correlations between number density errors of small (below 10 nm) and large (above 100 nm) particles. The evolving error covariances significantly improve the estimation accuracy of the Kalman Filter in case of synthetic observations.

Effect of Nucleation and Secondary Organic Aerosol Formation on Cloud Droplet Number Concentrations

The global general circulation model ECHAM5 is used together with HAM aerosol module to investigate the effect of the nucleation scheme on cloud droplet number concentrations. It is shown that nucleation can have a significant role on indirect aerosol effect. Also an efficient SOA formation scheme is intro- duced, and results are compared with original ECHAM5-HAM.