Abstract

In order to find an optimal convolution of the Advanced Microwave Sounding Unit (AMSU) -B to AMSU-A resolution the scan characteristics of AMSU-A and AMSU-B on board NOAA-15 are examined. A set of coefficients for this degradation is derived using the Backus–Gilbert technique. A 7 × 7 set of adjacent AMSU-B pixels is used where the center pixel is the one closest to a given AMSU-A observation. The error characteristics of the convolution are investigated and except for the two outermost footprints a good reproduction of the spatial sensitivity of the AMSU-A by the convolved AMSU-B is obtained. For a NOAA-15 overpass over inhomogeneous terrain AMSU-A data at 89 GHz were compared to convolved AMSU-B data at the same frequency. The root-mean-square deviation between the so-convolved AMSU-B data and the AMSU-A data was on average 1.7 K, including a systematic deviation of −1 K of AMSU-B to AMSU-A. In comparison, simple, equally weighted averages of AMSU-B data produce rms errors in the order of 4 K and large deviations in regions where gradients in the brightness temperatures occur.

To apply the Backus–Gilbert technique the AMSU’s effective field of view (EFOV) as a function of the scan position was determined. For the continuously scanning AMSU-B the integration time of 18 ms per observation in conjunction with the sensors rotation leads to a considerable broadening of the antenna pattern in cross-track direction and thus to an increase of the EFOV as compared to the instantaneous field of view (IFOV). This does not occur for the stepwise scanning AMSU-A where the IFOV equals the EFOV (neglecting the second-order effects induced by the ∼1-km movement of the subsatellite point during AMSU-A integration). Analytical expressions to calculate the AMSU-A and AMSU-B footprint sizes as functions of their respective scan positions were derived. These expressions exhibit rms deviations to the actual footprint size of 0.5 km with maximum deviations of less than 1 km.

Abstract

This study explores the utilization of Special Sensor Microwave/Imager (SSM/I) data in coastal regions where the measured signal consists of radiation received from both land and water surfaces. The problem of mixed land/water measurements (“footprints”) is solved using a high-resolution land–sea mask to infer the fraction of water surface for each measurement. A method to combine high-resolution datasets with SSM/I data is described in and its error characteristics are investigated. It is then used to derive the fraction of water surface within each SSM/I footprint from a high-resolution land–sea mask. The navigation uncertainty of the SSM/I was identified to be the dominant error source in data fusion. The method was applied to a two-month dataset of F11–SSM/I data for the Baltic Sea region. Based on this dataset the navigation uncertainty of the F11–SSM/I was quantified to be less than 7 km ().

In an algorithm is presented that corrects the coastal SSM/I measurements in such a way that retrieval algorithms designed for homogeneous water surfaces become applicable. However, the correction results in increased noise, 1.0–2.5 K, depending on frequency and polarization. The capability of the correction algorithm is demonstrated by comparing SSM/I estimates of columnar water vapor content with collocated coastal radiosounding measurements, yielding a root-mean-square deviation of 2.53 kg m⁻².

The method is utilized to derive monthly mean fields of columnar water vapor content over the Baltic Sea. The retrieved fields are compared to results of a numerical weather prediction model. There is a positive bias of the model results of about 1.2 kg m⁻² when compared with the SSM/I retrievals as well as with radiosoundings.

Abstract

The sensitivity of microwave brightness temperatures (TBs) to hydrometeors at frequencies between 89 and 190 GHz is investigated by comparing Fengyun-3C (FY-3C) Microwave Humidity Sounder-2 (MWHS-2) measurements with radar reflectivity profiles and retrieved products from the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (DPR). Scattering-induced TB depressions (ΔTBs), calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel, are compared with DPR-retrieved hydrometeor water path (HWP) and vertically integrated radar reflectivity Z _INT. We also account for the number of hydrometeors actually visible in each MWHS-2 channel by weighting HWP with the channel’s cloud-free gas transmission profile and the observation slant path. We denote these transmission-weighted, slant-path-integrated quantities with a superscript asterisk (e.g., HWP*). The so-derived linear sensitivity of ΔTB with respect to HWP* increases with frequency roughly to the power of 1.78. A retrieved HWP* of 1 kg m⁻² at 89 GHz on average corresponds to a decrease in observed TB, relative to a cloud-free background, of 11 K. At 183 GHz, the decrease is about 34–53 K. We perform a similar analysis using the vertically integrated, transmission-weighted slant-path radar reflectivity ${Z*}_{\mathrm{INT}}$ and find that ΔTB also decreases approximately linearly with ${\left({Z*}_{\mathrm{INT}}\right)}^{0.58}$ . The exponent of 0.58 corresponds to the one we find in the purely DPR-retrieval-based Z _INT–HWP relation. The observed sensitivities of ΔTB with respect to ${Z*}_{\mathrm{INT}}$ and HWP* allow for the validation of hydrometeor scattering models.

Abstract

A 20-yr (1986–2005) time series of Meteosat Visible and Infrared Imager (MVIRI) geostationary infrared observations was used to study deep convection over Africa and the tropical Atlantic. The 20-yr time period is covered by six consecutive satellites (Meteosat-2–7). To correct for possible systematic differences between instruments on the different satellite platforms, a time series of Meteosat infrared observations over cloud-free ocean surfaces was compared to reanalysis-based radiative transfer results. Based on the comparison of simulations with observations, a homogenization was performed for the MVIRI infrared channel. The homogenized 20-yr dataset was then subjected to a tracking analysis for deep convection over Africa and the tropical Atlantic for the boreal summer months of July–September.

The mean state of convection as well as anomalies for high– and low–Sahel rainfall years were studied. Comparisons with the Global Precipitation Climatology Center’s (GPCC) rainfall estimates were performed for the Sahel region and interannual variability was evaluated comparing convection for the five driest and five wettest Sahel years. Results support earlier findings that precipitation in the Sahel region is strongly linked to the latitudinal position of the African Easterly Jet with deep convection being triggered more strongly if the jet is displaced northward. A relationship between the jet position and long-lived convective systems over the tropical Atlantic was found as well.

Abstract

A dataset consisting of one year of CloudSat Cloud Profiling Radar (CPR) near-surface radar reflectivity Z associated with dry snowfall is examined in this study. The CPR observations are converted to snowfall rates S using derived Z_e –S relationships, which were created from backscatter cross sections of various nonspherical and spherical ice particle models. The CPR reflectivity histograms show that the dominant mode of global near-surface dry snowfall has extremely light reflectivity values (∼3–4 dBZ_e ), and an estimated 94% of all CPR dry snowfall observations are less than 10 dBZ_e . The average conditional global snowfall rate is calculated to be about 0.28 mm h⁻¹, but is regionally highly variable as well as strongly sensitive to the ice particle model chosen. Further, ground clutter contamination is found in regions of complex terrain even when a vertical reflectivity continuity threshold is utilized. The potential of future multifrequency spaceborne radars is evaluated using proxy 35–13.6-GHz reflectivities and sensor specifications of the proposed Global Precipitation Measurement dual-frequency precipitation radar (DPR). It is estimated that because of its higher detectability threshold, only about 7%–1% of the near-surface radar reflectivity values and about 17%–4% of the total accumulation associated with global dry snowfall would be detected by a DPR-like instrument, but these results are very sensitive to the chosen ice particle model. These potential detection shortcomings can be partially mitigated by using snowfall-rate distributions derived by the CPR or other similar high-frequency active sensors.

Abstract

This study investigates the effect of variable size distribution and density of precipitation ice particles on microwave brightness temperatures. For this purpose, a set of self-consistent relationships among rain rate, size parameters of an exponential drop size distribution, and the radar reflectivity–rain rate relations for frozen and liquid precipitation was derived. Further, a scaling factor was introduced that is the ratio between the average melted diameter of the frozen and liquid precipitation and allows the specification of different sizes of the frozen particles. For given radar observations, this method allows size distributions of frozen and liquid precipitation to be derived, which are then used as input for a radiative transfer model.

These relationships were used to perform Mie calculations for different precipitation rates and different types of hydrometeors (snow, graupel, and hail) and to investigate the dependence of their respective optical properties on rain rate as well as on the radar reflectivity. It is shown that, for a given rain rate, variations of particle density as well as of particle size may result in variations of the extinction coefficient by an order of magnitude. However, a comparison of volume extinction coefficients with radar reflectivities found that, for a broad range of particle sizes, the particle density has little effect in comparison with the liquid-equivalent size of the ice particles.

The proposed method was applied to cases of coincident Special Sensor Microwave Imager (SSM/I) and radar volume scans, the latter being provided by the Swedish Gotland radar. Microwave optical fields for all four SSM/I frequencies were derived from the radar data, and the observed brightness temperature was simulated using a three-dimensional Monte Carlo radiative transfer model. A comparison of scattering indices at 85 GHz derived from the SSM/I overpass with those derived from the model data found that the size of the precipitation-sized ice particles governs the variability of the scattering index. For the particular cases investigated here, the ice particle size varied considerably depending on the type of the precipitation event. For the case of an intensive thunderstorm, ice particles are roughly four times larger than liquid precipitation at the same rain rate, but the ice particles of a frontal system are inferred to be only 20% larger than liquid ice particles. A further evaluation of the relation between scattering index and radar-derived precipitation intensity above the freezing level found high correlations (0.8–0.9) for all precipitation events. However, the sensitivity of scattering index to precipitation intensity varies within a broad range for different types of precipitation events. A convective event had a sensitivity of 9 K (mm h⁻¹)⁻¹, but frontal and small-scale convective events showed sensitivities in the range between 20 and 50 K (mm h⁻¹)⁻¹.

Abstract

The assimilation of cloud- and rain-affected radiances in numerical weather prediction systems requires fast and accurate radiative transfer models. One of the largest sources of modeling errors originates from the assumptions regarding the vertical and horizontal subgrid-scale variability of model clouds and precipitation. In this work, cloud overlap assumptions are examined in the context of microwave radiative transfer and used to develop an accurate reference model. A fast cloud overlap algorithm is presented that allows for the accurate simulation of microwave radiances with a small number of radiative transfer calculations. In particular, the errors for a typical two-column approach currently used operationally are found to be relatively large for many cases of cloudy fields containing precipitation, even those with an overall cloud fraction of unity; these errors are largely eliminated by using the new approach presented here, at the cost of a slight increase in computation time. Radiative transfer cloud overlap errors are also evident in simulations when compared to actual satellite observations, in that the biases are somewhat reduced when applying a more accurate treatment of cloud overlap.

Abstract

A new method of deriving statistical moments related to the distribution of liquid water path over partially cloudy scenes is tested using a satellite cloud climatology. The method improves the ability to reconstruct total-scene visible reflectance when compared with an approach that relies on valid liquid water path retrievals, and thus it maintains physical consistency with the primary satellite observations when deriving cloud climatologies. A global application of the new method finds a mean bias of −0.008 ± 0.017 when reconstructing total-scene reflectance from liquid water path distributions, as compared with a bias of 0.05 ± 0.047 when using a conventional approach. Application of the method to a multidecadal cloud climatology suggests that this may provide a means of identifying data artifacts that could affect long-term cloud property trends. The conservation of reflectance plus the ease of applicability to various satellite datasets makes this method a valuable tool for model validation and comparison of satellite climatologies. Gaussian and gamma functions are used to approximate the distribution of horizontal subgrid-scale liquid water path for 1° × 1° scenes, and while both functions perform well for the majority of atmospheric conditions, it is found that the Gaussian distribution generates a negative bias for cases in which visible reflectance is very high and that neither function is able to represent liquid water path well in the few cases in which the observed distribution is bi- or multimodal.

Abstract

An important parameter often adjusted to achieve agreement between simulated and observed radiative fluxes in climate models is the rain formation efficiency. This adjustment has been justified as accounting for the effects of subgrid-scale variability in cloud properties, but this tuning approach is rather arbitrary. This study examines results from a regional climate model with precipitation formation schemes that have been conventionally tuned, and it compares them with simulations employing a recently developed scheme that uses satellite observations to explicitly account for the subgrid-scale variability of clouds (“integral constraint method”). Simulations with the International Pacific Research Center’s Regional Atmospheric Model (iRAM) show that the integral constraint method is capable of simulating cloud fields over the eastern Pacific that are in good agreement with observations, without requiring model tuning. A series of global warming simulations for late twenty-first-century conditions is performed to investigate the impact of the treatment of the precipitation formation efficiency on modeled cloud–climate feedbacks. The results with the integral constraint method show that the simulated cloud feedbacks have similar patterns at all the model resolutions considered (grid spacings of 50, 100, and 200 km), but there are some quantitative differences (with smaller feedbacks at finer resolution). The cloud responses to global warming in simulations with a conventionally tuned autoconversion scheme and with the integral constraint method were found to be quite consistent, although differences in individual regions of ~10%–30% are evident. No conclusions can be drawn from this study on the validity of model tuning for thick clouds and mixed phase or ice clouds, however.

Abstract

The retrieval of the mass-weighted mean diameter (D _m ) is a fundamental component of spaceborne precipitation retrievals. The Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) satellite is the first satellite to use dual-wavelength ratio measurements—the quotient of radar reflectivity factors (Z) measured at Ku and Ka wavelengths—to retrieve D _m . While it is established that DWR, being theoretically insensitive to changes in ice crystal mass and concentration, can provide a superior retrieval of D _m compared to Z-based retrievals, the benefits of this retrieval have yet to be directly observed or quantified. In this study, DWR–D _m and Z–D _m relationships are empirically generated from collocated airborne radar and in situ cloud particle probe measurements. Data are collected during nine intensive observation periods (IOPs) from three experiments representing different locations and times of year. Across IOPs with varying ice crystal concentrations, cloud temperatures, and storm types, Z–D _m relationships vary considerably while the DWR–D _m relationship remains consistent. This study confirms that a DWR–D _m relationship can provide a more accurate and consistent D _m retrieval than a Z–D _m relationship, quantified by a reduced overall RMSE (0.19 and 0.25 mm, respectively) and a reduced range of biases between experiments (0.11 and 0.32 mm, respectively).