Alan J. Geer

A Revised Cloud Overlap Scheme for Fast Microwave Radiative Transfer in Rain and Cloud

Abstract The assimilation of cloud- and precipitation-affected observations into weather forecasting systems requires very fast calculations of radiative transfer in the presence of multiple scattering. At the European Centre for Medium-Range Weather Forecasts (ECMWF), performance limitations mean that only a single cloudy calculation (including any precipitation) can be made, and the simulated radiance is a weighted combination of cloudy- and clear-sky radiances. Originally, the weight given to the cloudy part was the maximum cloud fraction in the atmospheric profile. However, this weighting was excessive, and because of nonlinear radiative transfer (the “beamfilling effect”) there were biases in areas of cloud and precipitation. A new approach instead uses the profile average cloud fraction, and decreases RMS errors by 40% in areas of rain or heavy clouds when “truth” comes from multiple independent column simulations. There is improvement all the way from low (e.g., 19 GHz) to high (e.g., 183 GHz) micr...

Solar Biases in Microwave Imager Observations Assimilated at ECMWF

The European Centre for Medium-Range Weather Forecasts (ECMWF) assimilates microwave imager observations for their information on humidity, cloud, and precipitation. However, Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) first-guess (FG) departure biases exhibit a 46-day oscillation with a peak-to-peak amplitude of up to 3 K in brightness temperature, which is linked to the precession of the equator crossing time of the TRMM orbit. The TMI bias has a diurnal cycle, but neither the Special Sensor Microwave Imager nor the Advanced Microwave Scanning Radiometer for the Earth Observing System shows a similar variation, so the bias must be in the TMI instrument itself. Its cause is probably solar heating of the main reflector, which is not perfectly reflective. This means that the instrument measures a combination of Earth emission and the physical temperature of the reflector. This effect has been partly corrected by the data providers, but their correction assumes a constant reflector temperature. In contrast, the ECMWF FG departures suggest that the reflector temperature varies by up to 70 K through the orbit. A method is presented for correcting the bias in the ECMWF assimilation system. It is also noted that when building future conical-scanning microwave imagers, it is important to provide reflectors that are as nonemissive as possible and to also carefully record the reflectors actual (possibly frequency-dependent) emissivity. The in-space temperature of the reflector surface should also be recorded so that it can be used in bias correction.

Significance of changes in medium-range forecast scores

The impact of developments in weather forecasting is measured using forecast verification, but many developments, though useful, have impacts of less than 0.5 % on medium-range forecast scores. Chaotic variability in the quality of individual forecasts is so large that it can be hard to achieve statistical significance when comparing these ‘smaller’ developments to a control. For example, with 60 separate forecasts and requiring a 95 % confidence level, a change in quality of the day-5 forecast needs to be larger than 1 % to be statistically significant using a Students t-test. The first aim of this study is simply to illustrate the importance of significance testing in forecast verification, and to point out the surprisingly large sample sizes that are required to attain significance. The second aim is to see how reliable are current approaches to significance testing, following suspicion that apparently significant results may actually have been generated by chaotic variability. An independent realisation of the null hypothesis can be created using a forecast experiment containing a purely numerical perturbation, and comparing it to a control. With 1885 paired differences from about 2.5 yr of testing, an alternative significance test can be constructed that makes no statistical assumptions about the data. This is used to experimentally test the validity of the normal statistical framework for forecast scores, and it shows that the naive application of Students t-test does generate too many false positives (i.e. false rejections of the null hypothesis). A known issue is temporal autocorrelation in forecast scores, which can be corrected by an inflation in the size of confidence range, but typical inflation factors, such as those based on an AR(1) model, are not big enough and they are affected by sampling uncertainty. Further, the importance of statistical multiplicity has not been appreciated, and this becomes particularly dangerous when many experiments are compared together. For example, across three forecast experiments, there could be roughly a 1 in 2 chance of getting a false positive. However, if correctly adjusted for autocorrelation, and when the effects of multiplicity are properly treated using a Šidák correction, the t-test is a reliable way of finding the significance of changes in forecast scores.

Evaluation of Radiative Transfer Models With Clouds

Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud-free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium-Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm 1 have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm 1 at night are reasonably consistent with results at 900 cm . Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm 1 are inferior to those at 900 cm 1 for daytime calculations. Plain Language Summary Getting the right clouds of the right type, at the right time and location in Global Circulation Models, is key to getting the local energy balance right. This is key to an accurate forecast. If the clouds are of the wrong type or at the wrong location or time, the accuracy of the forecast is degraded. We evaluate the accuracy of the best currently available cloud description (produced by the European Center for Medium-Range Weather Forecasting) by comparing the radiances calculated using Radiative Transfer Models (RTMs) from six major development teams to cloudy radiances observed by the Atmospheric Infrared Sounder at the same location and time. The better RTMs fit statistically reasonably well in the 11-μm atmospheric window area, with little latitude (zonal) and day/night cloud-type related bias. None of the RTMs fit well in the 4-μm atmospheric window area during daytime, unless the calculations use full scattering. With the current state of art, all major RTMs would be suitable to start the validation of cloud effects in the National Weather Center models using just one 11-μm atmospheric window channel.

Summer Snowfall Workshop: Scattering Properties of Realistic Frozen Hydrometeors from Simulations and Observations, as well as Defining a New Standard for Scattering Databases

(Beginning of WHAT, WHEN, WHERE Summary Box:) What: The work-shop gathered almost 50 scientists from Europe and the United States to discuss the progress towards developing electromagnetic scattering databases for ice and snow particles in the microwave region, their applications, the physical approximations used to compute these scattering properties, and how remote sensing and in situ observations can be used to validate scattering datasets. One of the main priorities of the workshop was to foster communication between users and developers of scattering databases, and to define standards and conventions for scattering data structures and variables. When: 28-30 June 2017. Where: Cologne, Germany (END of what, when, where summary box).

An update on the RTTOV fast radiative transfer model (currently at version 12)

This paper gives an update of the RTTOV (Radiative Transfer for TOVS) fast radiative transfer model, which is widely used in the satellite retrieval and data assimilation communities. RTTOV is a fast radiative transfer model for simulating top-of-atmosphere radiances from passive visible, infrared and microwave downward-viewing satellite radiometers. In addition to the forward model, it also optionally computes the tangent linear, adjoint and Jacobian matrix providing changes in radiances for profile variable perturbations assuming a linear relationship about a given atmospheric state. This makes it a useful tool for developing physical retrievals from satellite radiances, for direct radiance assimilation in NWP models, for simulating future instruments, and for training or teaching with a graphical user interface. An overview of the RTTOV model is given, highlighting the updates and increased capability of the latest versions, and it gives some examples of its current performance when compared with more accurate line-by-line radiative transfer models and a few selected observations. The improvement over the original version of the model released in 1999 is demonstrated.

Assessing the impact of different liquid water permittivity models onthe fit between model and observations

Permittivity models for microwave frequencies of liquid water below 0 C (supercooled liquid water) are poorly constrained due to limited laboratory experiments and observations, especially for high microwave frequencies. This uncertainty translates directly into errors in retrieved liquid water paths of up to 80 %. This study investigates the effect of different liquid water permittivity models on simulated brightness temperatures by using the all-sky assimilation framework of the Integrated Forecast System. Here, a model configuration with an improved representation of supercooled liquid water has been used. The comparison of five different permittivity models with the current one shows a small mean reduction in simulated brightness temperatures of at most 0.15 K at 92 GHz on a global monthly scale. During austral winter, differences occur more prominently in the storm tracks of the Southern Hemisphere and in the intertropical convergence zone with values of around 0.5 to 1.5 K. Compared to the default Liebe (1989) approach, the permittivity models of Stogryn et al. (1995), Rosenkranz (2015) and Turner et al. (2016) all improve fits between observations and all-sky brightness temperatures simulated by the Integrated Forecast System. In cycling data assimilation these newer models also give small improvements in short-range humidity forecasts when measured against independent observations. Of the three best-performing models, the Stogryn et al. (1995) model is not quite as beneficial as the other two, except at 183 GHz. At this frequency, Rosenkranz (2015) and Turner et al. (2016) look worse because they expose a scattering-related forward model bias in frontal regions. Overall, Rosenkranz (2015) is favoured due to its validity up to 1 THz, which will support future submillimetre missions.