Forecast indices from a ground-based microwave radiometer for operational meteorology

Cimini, Domenico; Nelson, Marta; Güldner, J.; Ware, Randolph

doi:10.5194/amt-8-315-2015

Cited by 69 publications

(44 citation statements)

References 35 publications

(59 reference statements)

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“…4 and Table 2 confirms that the rms errors are smaller than the uncertainty associated with TB observations for all channels and all elevation angles. The previous tests against the reference LBL R98 model have also been performed at the 22 frequency channels (22-60 GHz) used by another commercial microwave radiometer, the MP-3000A (Cimini et al, 2011(Cimini et al, , 2015. Statistics, reported in Table 3 in terms of bias and rms, are similar to those obtained for HATPRO channels, at both K-and V-band.…”

Section: Comparison With Line-by-line Model Computed Radiancesupporting

confidence: 59%

RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations

et al. 2016

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Abstract. Ground-based microwave radiometers (MWRs) offer a new capability to provide continuous observations of the atmospheric thermodynamic state in the planetary boundary layer. Thus, they are potential candidates to supplement radiosonde network and satellite data to improve numerical weather prediction (NWP) models through a variational assimilation of their data. However in order to assimilate MWR observations, a fast radiative transfer model is required and such a model is not currently available. This is necessary for going from the model state vector space to the observation space at every observation point. The fast radiative transfer model RTTOV is well accepted in the NWP community, though it was developed to simulate satellite observations only. In this work, the RTTOV code has been modified to allow for simulations of ground-based upwardlooking microwave sensors. In addition, the tangent linear, adjoint, and K-modules of RTTOV have been adapted to provide Jacobians (i.e., the sensitivity of observations to the atmospheric thermodynamical state) for ground-based geometry. These modules are necessary for the fast minimization of the cost function in a variational assimilation scheme. The proposed ground-based version of RTTOV, called RTTOVgb, has been validated against accurate and less time-efficient line-by-line radiative transfer models. In the frequency range commonly used for temperature and humidity profiling (22-60 GHz), root-mean-square brightness temperature differences are smaller than typical MWR uncertainties ( ∼ 0.5 K) at all channels used in this analysis. Brightness temperatures (TBs) computed with RTTOV-gb from radiosonde profiles have been compared with nearly simultaneous and co-located ground-based MWR observations. Differences between simulated and measured TBs are below 0.5 K for all channels except for the water vapor band, where most of the uncertainty comes from instrumental errors. The Jacobians calculated with the K-module of RTTOV-gb have been compared with those calculated with the brute force technique and those from the line-by-line model ARTS. Jacobians are found to be almost identical, except for liquid water content Jacobians for which a 10 % difference between ARTS and RTTOV-gb at transparent channels around 450 hPa is attributed to differences in liquid water absorption models. Finally, RTTOVgb has been applied as the forward model operator within a one-dimensional variational (1D-Var) software tool in an Observing System Simulation Experiment (OSSE). For both temperature and humidity profiles, the 1D-Var with RTTOVgb improves the retrievals with respect to the NWP model in the first few kilometers from the ground.

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Section: Comparison With Line-by-line Model Computed Radiancesupporting

confidence: 59%

RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations

et al. 2016

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“…Moreover, the MWR is equipped with an infrared radiation thermometer (IRT), which measures sky infrared temperature at one zenith infrared (9.6-10.5 µm) channel and gives information on cloud-base temperature (Ware et al, 2013;Cimini et al, 2015;Xu et al, 2015). Meteorological sensors attached to the MWR can obtain ambient temperature, pressure, and relative humidity at the instrument level.…”

Section: Methodsmentioning

confidence: 99%

“…To minimize the influence of liquid water on MWR measurements, the MWR is equipped with an inverted "U"-shaped hydrophobic radome and a special blower system, which can sweep water beads and snow away from the radome (Chan, 2009). Recently, off-zenith observation has been applied in MWR observations, and off-zenith retrievals provide higher accuracy during precipitation by minimizing the effect of liquid water on the radiometer radome (Cimini et al, 2011(Cimini et al, , 2015Ware et al, 2013;Xu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…These profiles are available nearly continuously and are extensively utilized in the forecasting and analysis of intense convective weather; also they have been assimilated into numerical weather prediction models (Marzano et al, 2005;Knupp et al, 2009;Löhnert and Maier, 2012;Madhulatha et al, 2013). The instability indices calculated from the MWR-retrieved thermodynamic atmospheric profiles are also employed in operational meteorology (Chan and Hon, 2010;Cimini et al, 2015;Leena et al, 2015). With the purpose of improving the MWR measurement accuracy, some procedures and methods are performed.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith observations under snow conditions

Zhang

Liu³

et al. 2017

Atmos. Meas. Tech.

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Abstract. This paper is to investigate the uncertainties of microwave radiometer (MWR) retrievals in snow conditions and also explore the discrepancies of MWR retrievals in zenith and off-zenith observations. The MWR retrievals were averaged in a ±15 min period centered at sounding times of 00:00 and 12:00 UTC and compared with radiosonde observations (RAOBs). In general, the MWR retrievals have a better correlation with RAOB profiles in off-zenith observations than in zenith observations, and the biases (MWR observations minus RAOBs) and root mean square errors (RMSEs) between MWR and RAOB are also clearly reduced in off-zenith observations. The biases of temperature, relative humidity, and vapor density decrease from 4.6 K, 9 %, and 1.43 g m −3 in zenith observations to −0.6 K, −2 %, and 0.10 g m −3 in off-zenith observations, respectively. The discrepancies between MWR retrievals and RAOB profiles by altitude present the same situation. Cases studies show that the impact of snow on accuracies of MWR retrievals is more serious in heavy snowfall than in light snowfall, but off-zenith observation can mitigate the impact of snowfall. The MWR measurements become less accurate in snowfall mainly due to the retrieval algorithm, which does not consider the effect of snow, and the accumulated snow on the top of the radome increases the signal noise of MWR measurements. As the snowfall drops away by gravity on the sides of the radome, the off-zenith observations are more representative of the atmospheric conditions for RAOBs.

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“…The potential benefits of ground-based remote sensing instruments such as ceilometer, cloud radar, wind profiler, and passive radiometers are quite well understood and have attracted attention to the continuous efforts for an improvement (Wilczak et al, 1998;WMO, 2006;Cimini et al, 2015). Among these instruments, the ground-based microwave sounding radiometer (hereafter the radiometer) which takes measurement of the downwelling radiances (in the form of brightness temperature, T b ) in the microwave region has been used to obtain the vertical information of temperature (T ) and humidity (q) Löhnert and Maier, 2012;Cadeddu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Characterization of downwelling radiance measured from a ground-based microwave radiometer using numerical weather prediction model data

et al. 2016

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Abstract. The ground-based microwave sounding radiometers installed at nine weather stations of Korea Meteorological Administration alongside with the wind profilers have been operating for more than 4 years. Here we apply a process to assess the characteristics of the observation data by comparing the measured brightness temperature (T b ) with reference data. For the current study, the reference data are prepared by the radiative transfer simulation with the temperature and humidity profiles from the numerical weather prediction model instead of the conventional radiosonde data. Based on the 3 years of data, from 2010 to 2012, we were able to characterize the effects of the absolute calibration on the quality of the measured T b . We also showed that when clouds are present the comparison with the model has a high variability due to presence of cloud liquid water therefore making cloudy data not suitable for assessment of the radiometer's performance. Finally we showed that differences between modeled and measured brightness temperatures are unlikely due to a shift in the selection of the center frequency but more likely due to spectroscopy issues in the wings of the 60 GHz absorption band. With a proper consideration of data affected by these two effects, it is shown that there is an excellent agreement between the measured and simulated T b . The regression coefficients are better than 0.97 along with the bias value of better than 1.0 K except for the 52.28 GHz channel which shows a rather large bias and variability of − 2.6 and 1.8 K, respectively.

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Forecast indices from a ground-based microwave radiometer for operational meteorology

Cited by 69 publications

References 35 publications

RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations

RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations

Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith observations under snow conditions

Characterization of downwelling radiance measured from a ground-based microwave radiometer using numerical weather prediction model data

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