Abstract

An instrument for measuring the size and concentration of raindrops is described which has the ability to function equally well in calm conditions and strong winds. Raindrops are detected optically in a shadowgraph-type imaging system. A unique feature of the device is the sensing of drop size as drops enter and leave a cylindrical sample volume. Each drop produces two equally sized pulses, the heights of which are proportional to the drop's diameter and their separation yields the drop's transit time. The drop concentration can be derived from this data without assuming or measuring drop velocities. The instrument has a large sample volume, but a negligible loss of data resulting from two drops being sampled simultaneously. Drops above 300 μm can be detected, but beam divergence limits accurate sizing to drops larger than 400 μm. Laboratory calibration shows that drops greater than 1 mm in diameter may be sized to better than 5%. Comparison tests with a tipping bucket raingage reveal that time integrations of the raindrop size spectra yield rainfall totals generally within 10% of the bulk-measured values. These tests also show that disdrometers which sense raindrop flux win seriously overestimate drop concentrations in windy conditions but that this new device performs satisfactorily for winds of up to 20 m s⁻¹.

Abstract

The differential reflectivity (Z _DR) measures the mean shape of hydrometeors and provides an estimate of the mean size of raindrops Observations of Z _DR for rain may be combined with the conventional radar reflectivity factor (Z) and fitted to any two-parameter raindrop size distribution and this information used to derive more accurate rainfall rates. In such work the precise shape of raindrops is a critical parameter. Recently available data suggest that large raindrops are more oblate than previously believed. These new shapes support the idea that Z _DR values above 3.5 dB can be attributed to rain. Average values of Z _DR as a function of Z obtained in heavy rain by the Chilbolton radar agree very closely with those predicted using the new shapes. Statistics are also presented of the natural variability of raindrop spectra in heavy rain. Analytic expressions are proposed for computing rainfall rate from Z and Z _DR.

Abstract

Radar refractivity retrievals have the potential to accurately capture near-surface humidity fields from the phase change of ground clutter returns. In practice, phase changes are very noisy and the required smoothing will diminish large radial phase change gradients, leading to severe underestimates of large refractivity changes (ΔN). To mitigate this, the mean refractivity change over the field (〈ΔN〉_field) must be subtracted prior to smoothing. However, both observations and simulations indicate that highly correlated returns (e.g., when single targets straddle neighboring gates) result in underestimates of 〈ΔN〉_field when pulse-pair processing is used. This may contribute to reported differences of up to 30 N units between surface observations and retrievals. This effect can be avoided if 〈ΔN〉_field is estimated using a linear least squares fit to azimuthally averaged phase changes. Nevertheless, subsequent smoothing of the phase changes will still tend to diminish the all-important spatial perturbations in retrieved refractivity relative to 〈ΔN〉_field; an iterative estimation approach may be required. The uncertainty in the target location within the range gate leads to additional phase noise proportional to ΔN, pulse length, and radar frequency. The use of short pulse lengths is recommended, not only to reduce this noise but to increase both the maximum detectable refractivity change and the number of suitable targets. Retrievals of refractivity fields must allow for large ΔN relative to an earlier reference field. This should be achievable for short pulses at S band, but phase noise due to target motion may prevent this at C band, while at X band even the retrieval of ΔN over shorter periods may at times be impossible.

Abstract

Radar refractivity retrievals can capture near-surface humidity changes, but noisy phase changes of the ground clutter returns limit the accuracy for both klystron- and magnetron-based systems. Observations with a C-band (5.6 cm) magnetron weather radar indicate that the correction for phase changes introduced by local oscillator frequency changes leads to refractivity errors no larger than 0.25 N units: equivalent to a relative humidity change of only 0.25% at 20°C. Requested stable local oscillator (STALO) frequency changes were accurate to 0.002 ppm based on laboratory measurements. More serious are the random phase change errors introduced when targets are not at the range-gate center and there are changes in the transmitter frequency (Δf _Tx) or the refractivity (ΔN). Observations at C band with a 2-μs pulse show an additional 66° of phase change noise for a Δf _Tx of 190 kHz (34 ppm); this allows the effect due to ΔN to be predicted. Even at S band with klystron transmitters, significant phase change noise should occur when a large ΔN develops relative to the reference period [e.g., ~55° when ΔN = 60 for the Next Generation Weather Radar (NEXRAD) radars]. At shorter wavelengths (e.g., C and X band) and with magnetron transmitters in particular, refractivity retrievals relative to an earlier reference period are even more difficult, and operational retrievals may be restricted to changes over shorter (e.g., hourly) periods of time. Target location errors can be reduced by using a shorter pulse or identified by a new technique making alternate measurements at two closely spaced frequencies, which could even be achieved with a dual–pulse repetition frequency (PRF) operation of a magnetron transmitter.

Abstract

The copolar correlation coefficient ρ _hv has many applications, including hydrometeor classification, ground clutter and melting-layer identification, interpretation of ice microphysics, and the retrieval of raindrop size distributions (DSDs). However, the quantitative error estimates that are necessary if these applications are to be fully exploited are currently lacking. Previous error estimates of ρ _hv rely on knowledge of the unknown “true” ρ _hv and implicitly assume a Gaussian probability distribution function of ρ _hv samples. Frequency distributions of ρ _hv estimates are in fact shown to be highly negatively skewed. A new variable, = log₁₀(1 − ρ _hv), is defined that does have Gaussian error statistics and a standard deviation depending only on the number of independent radar pulses. This is verified using observations of spherical drizzle drops, allowing, for the first time, the construction of rigorous confidence intervals in estimates of ρ _hv. In addition, the manner in which the imperfect collocation of the horizontal and vertical polarization sample volumes may be accounted for is demonstrated. The possibility of using L to estimate the dispersion parameter μ in the gamma drop size distribution is investigated. Including drop oscillations is found to be essential for this application; otherwise, there could be biases in retrieved μ of up to approximately 8. Preliminary results in rainfall are presented. In a convective rain case study, the estimates presented herein show μ to be substantially larger than 0 (an exponential DSD). In this particular rain event, rain rate would be overestimated by up to 50% if a simple exponential DSD is assumed.

Abstract

The purpose of this paper is to assess the potential of a spaceborne 94-GHz radar for providing useful measurements of the vertical distribution and water content of ice clouds on a global scale.

Calculations of longwave (LW) fluxes for a number of model ice clouds are performed. These are used to determine the minimum cloud optical depth that will cause changes in the outgoing longwave radiation or flux divergence within a cloud layer greatear than 10 W m⁻², and in surface downward LW flux greater than 5 W m⁻², compared to the clear-sky value. These optical depth values are used as the definition of a “radiatively significant” cloud. Different “thresholds of radiative significance” are calculated for each of the three radiation parameters and also for tropical and midlatitude cirrus clouds. Extensive observational datasets of ice crystal size spectra from midlatitude and tropical cirrus are then used to assess the capability of a radar to meet these measurement requirements. A radar with a threshold of −30 dBZ should detect 99% (92%) of “radiatively significant” clouds in the midlatitudes (Tropics). This detection efficiency may be reduced significantly for tropical clouds at very low temperatures (−80°C).

The LW flux calculations are also used to establish the required accuracy within which the optical depth should be known in order to estimate LW fluxes or flux divergence to within specified limits of accuracy. Accuracy requirements are also expressed in terms of ice water content (IWC) because of the need to validate cloud parameterization schemes in general circulation models (GCMs). Estimates of IWC derived using radar alone and also using additional information to define the mean crystal size are considered. With crystal size information available, the IWC for samples with a horizontal scale of 1–2 km may be obtained with a bias of less than 8%. For IWC larger than 0.01 g m⁻³, the random error is in the range +50% to −35%, whereas for a value of 0.001 g m⁻³ the random error increases to between +80% and −45%. This level of accuracy also represents the best that may be achieved for estimates of the cloud optical depth and meets the requirements derived from LW flux calculations. In the absence of independent particle size information, the random error is within the range +85% to −55% for IWC greater than 0.01 g m⁻³. For the same IWC range, the estimated bias is few than ±15%. This accuracy is sufficient to provide useful constraints on GCM cloud parameteriation schemes.

Abstract

The assimilation of Doppler radar radial winds for high-resolution NWP may improve short-term forecasts of convective weather. Using insects as the radar target, it is possible to provide wind observations during convective development. This study aims to explore the potential of these new observations, with three case studies. Radial winds from insects detected by four operational weather radars were assimilated using three-dimensional variational data assimilation (3D-Var) into a 1.5-km resolution version of the Met Office Unified Model, using a southern U.K. domain and no convective parameterization. The effect on the analyzed wind was small, with changes in direction and speed up to 45° and 2 m s⁻¹, respectively. The forecast precipitation was perturbed in space and time but not substantially modified. Radial wind observations from insects show the potential to provide small corrections to the location and timing of showers, but not to completely relocate convergence lines. Overall, quantitative analysis indicated the observation impact in the three case studies was small and neutral. However, the small sample size and possible ground clutter contamination issues preclude unequivocal impact estimation. The study shows the potential positive impact of insect winds; future operational systems using dual-polarization radars that are better able to discriminate between insects and clutter returns should provide a much greater impact on forecasts.

Abstract

A new generation of high-resolution (1 km) forecast models promises to revolutionize the prediction of hazardous weather such as windstorms, flash floods, and poor air quality. To realize this promise, a dense observing network, focusing on the lower few kilometers of the atmosphere, is required to verify these new forecast models with the ultimate goal of assimilating the data. At present there are insufficient systematic observations of the vertical profiles of water vapor, temperature, wind, and aerosols; a major constraint is the absence of funding to install new networks. A recent research program financed by the European Union, tasked with addressing this lack of observations, demonstrated that the assimilation of observations from an existing wind profiler network reduces forecast errors, provided that the individual instruments are strategically located and properly maintained. Additionally, it identified three further existing European networks of instruments that are currently underexploited, but with minimal expense they could deliver quality-controlled data to national weather services in near–real time, so the data could be assimilated into forecast models. Specifically, 1) several hundred automatic lidars and ceilometers can provide backscatter profiles associated with aerosol and cloud properties and structures with 30-m vertical resolution every minute; 2) more than 20 Doppler lidars, a fairly new technology, can measure vertical and horizontal winds in the lower atmosphere with a vertical resolution of 30 m every 5 min; and 3) about 30 microwave profilers can estimate profiles of temperature and humidity in the lower few kilometers every 10 min. Examples of potential benefits from these instruments are presented.

Abstract

To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

The Cloudnet project aims to provide a systematic evaluation of clouds in forecast and climate models by comparing the model output with continuous ground-based observations of the vertical profiles of cloud properties. In the models, the properties of clouds are simplified and expressed in terms of the fraction of the model grid box, which is filled with cloud, together with the liquid and ice water content of the clouds. These models must get the clouds right if they are to correctly represent both their radiative properties and their key role in the production of precipitation, but there are few observations of the vertical profiles of the cloud properties that show whether or not they are successful. Cloud profiles derived from cloud radars, ceilometers, and dual-frequency microwave radiometers operated at three sites in France, Netherlands, and the United Kingdom for several years have been compared with the clouds in seven European models. The advantage of this continuous appraisal is that the feedback on how new versions of models are performing is provided in quasi-real time, as opposed to the much longer time scale needed for in-depth analysis of complex field studies. Here, two occasions are identified when the introduction of new versions of the ECMWF and Météo-France models leads to an immediate improvement in the representation of the clouds and also provides statistics on the performance of the seven models. The Cloudnet analysis scheme is currently being expanded to include sites outside Europe and further operational forecasting and climate models.