2021 Vol. 38, No. 8
Display Method:
2021, 38(8): 1267-1284.
doi: 10.1007/s00376-021-0425-3
Abstract:
Following the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.
Following the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.
2021, 38(8): 1285-1298.
doi: 10.1007/s00376-020-0207-3
Abstract:
This paper reviews the data quality and impact of observations from the FY-3 satellite series used operationally in the ECMWF system. This includes data from the passive microwave radiometers MWHS-1, MWHS-2 and MWRI, as well as observations from the radio occultation receiver GNOS. Evaluations against background equivalents show that the quality of the observations is broadly comparable to that of similar instruments on other polar-orbiting satellites, even though biases for the passive microwave observations can be somewhat larger and more complex for some channels. An observing system experiment shows that the FY-3 instruments jointly contribute significantly to the forecast skill in the ECMWF system. Positive impact of up to 2% is seen for most variables out to the day-2 forecasts over hemispheric scales, with significant benefits for total column water vapor or for temperature and wind in the stratosphere out to day 4.
This paper reviews the data quality and impact of observations from the FY-3 satellite series used operationally in the ECMWF system. This includes data from the passive microwave radiometers MWHS-1, MWHS-2 and MWRI, as well as observations from the radio occultation receiver GNOS. Evaluations against background equivalents show that the quality of the observations is broadly comparable to that of similar instruments on other polar-orbiting satellites, even though biases for the passive microwave observations can be somewhat larger and more complex for some channels. An observing system experiment shows that the FY-3 instruments jointly contribute significantly to the forecast skill in the ECMWF system. Positive impact of up to 2% is seen for most variables out to the day-2 forecasts over hemispheric scales, with significant benefits for total column water vapor or for temperature and wind in the stratosphere out to day 4.
2021, 38(8): 1315-1333.
doi: 10.1007/s00376-021-0443-1
Abstract:
High spectral resolution (or hyperspectral) infrared (IR) sounders onboard low earth orbiting satellites provide high vertical resolution atmospheric information for numerical weather prediction (NWP) models. In contrast, imagers on geostationary (GEO) satellites provide high temporal and spatial resolution which are important for monitoring the moisture associated with severe weather systems, such as rapidly developing local severe storms (LSS). A hyperspectral IR sounder onboard a geostationary satellite would provide four-dimensional atmospheric temperature, moisture, and wind profiles that have both high vertical resolution and high temporal/spatial resolutions. In this work, the added-value from a GEO-hyperspectral IR sounder is studied and discussed using a hybrid Observing System Simulation Experiment (OSSE) method. A hybrid OSSE is distinctively different from the traditional OSSE in that, (a) only future sensors are simulated from the nature run and (b) the forecasts can be evaluated using real observations. This avoids simulating the complicated observation characteristics of the current systems (but not the new proposed system) and allows the impact to be assessed against real observations. The Cross-track Infrared Sounder (CrIS) full spectral resolution (FSR) is assumed to be onboard a GEO for the impact studies, and the GEO CrIS radiances are simulated from the ECMWF Reanalysis v5 (ERA5) with the hyperspectral IR all-sky radiative transfer model (HIRTM). The simulated GEO CrIS radiances are validated and the hybrid OSSE system is verified before the impact assessment. Two LSS cases from 2018 and 2019 are selected to evaluate the value-added impacts from the GEO CrIS-FSR data. The impact studies show improved atmospheric temperature, moisture, and precipitation forecasts, along with some improvements in the wind forecasts. An added-value, consisting of an overall 5% Root Mean Square Error (RMSE) reduction, was found when a GEO CrIS-FSR is used in replacement of LEO ones indicating the potential for applications of data from a GEO hyperspectral IR sounder to improve local severe storm forecasts.
High spectral resolution (or hyperspectral) infrared (IR) sounders onboard low earth orbiting satellites provide high vertical resolution atmospheric information for numerical weather prediction (NWP) models. In contrast, imagers on geostationary (GEO) satellites provide high temporal and spatial resolution which are important for monitoring the moisture associated with severe weather systems, such as rapidly developing local severe storms (LSS). A hyperspectral IR sounder onboard a geostationary satellite would provide four-dimensional atmospheric temperature, moisture, and wind profiles that have both high vertical resolution and high temporal/spatial resolutions. In this work, the added-value from a GEO-hyperspectral IR sounder is studied and discussed using a hybrid Observing System Simulation Experiment (OSSE) method. A hybrid OSSE is distinctively different from the traditional OSSE in that, (a) only future sensors are simulated from the nature run and (b) the forecasts can be evaluated using real observations. This avoids simulating the complicated observation characteristics of the current systems (but not the new proposed system) and allows the impact to be assessed against real observations. The Cross-track Infrared Sounder (CrIS) full spectral resolution (FSR) is assumed to be onboard a GEO for the impact studies, and the GEO CrIS radiances are simulated from the ECMWF Reanalysis v5 (ERA5) with the hyperspectral IR all-sky radiative transfer model (HIRTM). The simulated GEO CrIS radiances are validated and the hybrid OSSE system is verified before the impact assessment. Two LSS cases from 2018 and 2019 are selected to evaluate the value-added impacts from the GEO CrIS-FSR data. The impact studies show improved atmospheric temperature, moisture, and precipitation forecasts, along with some improvements in the wind forecasts. An added-value, consisting of an overall 5% Root Mean Square Error (RMSE) reduction, was found when a GEO CrIS-FSR is used in replacement of LEO ones indicating the potential for applications of data from a GEO hyperspectral IR sounder to improve local severe storm forecasts.
2021, 38(8): 1334-1350.
doi: 10.1007/s00376-021-0337-2
Abstract:
The accuracy of passive satellite cloud top height (CTH) retrieval shows regional dependence. This paper assesses the CTH derived from the FY-4A and Himawari-8 satellites through comparison with those from the ground-based millimeter radar at two sites: Yangbajing, Tibet, China (YBJ), and the Institute of Atmospheric Physics (IAP), Beijing, China. The comparison shows that Himawari-8 missed more CTHs at night than FY-4A, especially at YBJ. It is found that the CTH difference (CTHD; radar CTH minus satellite CTH) for FY-4A and Himawari-8 is 0.06 ± 1.90 km and −0.02 ± 2.40 km at YBJ respectively, and that is 0.93 ± 2.24 km and 0.99 ± 2.37 km at IAP respectively. The discrepancy between the satellites and radar at IAP is larger than that at YBJ. Both satellites show better performance for mid-level and low-level clouds than for high-level clouds at the two sites. The retrievals from FY-4A agree well with those from Himawari-8, with a mean difference of 0.08 km at YBJ and 0.06 km at IAP. It is found that the CTHD decreases as the cloud depth increases at both sites. However, the CTHD has no obvious dependence on cloud layers and fractions. Investigations show that aerosol concentration has little impact on the CTHD. For high and thin clouds, the CTHD increases gradually with the increase of the surface temperature, which might be a key factor causing the regional discrepancy between IAP and YBJ.
The accuracy of passive satellite cloud top height (CTH) retrieval shows regional dependence. This paper assesses the CTH derived from the FY-4A and Himawari-8 satellites through comparison with those from the ground-based millimeter radar at two sites: Yangbajing, Tibet, China (YBJ), and the Institute of Atmospheric Physics (IAP), Beijing, China. The comparison shows that Himawari-8 missed more CTHs at night than FY-4A, especially at YBJ. It is found that the CTH difference (CTHD; radar CTH minus satellite CTH) for FY-4A and Himawari-8 is 0.06 ± 1.90 km and −0.02 ± 2.40 km at YBJ respectively, and that is 0.93 ± 2.24 km and 0.99 ± 2.37 km at IAP respectively. The discrepancy between the satellites and radar at IAP is larger than that at YBJ. Both satellites show better performance for mid-level and low-level clouds than for high-level clouds at the two sites. The retrievals from FY-4A agree well with those from Himawari-8, with a mean difference of 0.08 km at YBJ and 0.06 km at IAP. It is found that the CTHD decreases as the cloud depth increases at both sites. However, the CTHD has no obvious dependence on cloud layers and fractions. Investigations show that aerosol concentration has little impact on the CTHD. For high and thin clouds, the CTHD increases gradually with the increase of the surface temperature, which might be a key factor causing the regional discrepancy between IAP and YBJ.
2021, 38(8): 1351-1366.
doi: 10.1007/s00376-020-0174-8
Abstract:
Water vapor plays a key role in weather, climate and environmental research on local and global scales. Knowledge about atmospheric water vapor and its spatiotemporal variability is essential for climate and weather research. Because of the advantage of a unique temporal and spatial resolution, satellite observations provide global or regional water vapor distributions. The advanced Medium Resolution Spectral Imager (MERSI) instrument—that is, MERSI-II—onboard the Fengyun-3D (FY-3D) meteorological satellite, has been one of the major satellite sensors routinely providing precipitable water vapor (PWV) products to the community using near-infrared (NIR) measurements since June 2018. In this paper, the major updates related to the production of the NIR PWV products of MERSI-II are discussed for the first time. In addition, the water vapor retrieval algorithm based on the MERSI-II NIR channels is introduced and derivations are made over clear land areas, clouds, and sun-glint areas over the ocean. Finally, the status and samples of the MERSI-II PWV products are presented. The accuracy of MERSI-II PWV products is validated using ground-based GPS measurements. The results show that the accuracies of the water vapor products based on the updated MERSI-II instrument are significantly improved compared with those of MERSI, because MERSI-II provides a better channel setting and new calibration method. The root-mean-square error and relative bias of MERSI-II PWV products are typically 1.8–5.5 mm and −3.0% to −14.3%, respectively, and thus comparable with those of other global remote sensing products of the same type.
Water vapor plays a key role in weather, climate and environmental research on local and global scales. Knowledge about atmospheric water vapor and its spatiotemporal variability is essential for climate and weather research. Because of the advantage of a unique temporal and spatial resolution, satellite observations provide global or regional water vapor distributions. The advanced Medium Resolution Spectral Imager (MERSI) instrument—that is, MERSI-II—onboard the Fengyun-3D (FY-3D) meteorological satellite, has been one of the major satellite sensors routinely providing precipitable water vapor (PWV) products to the community using near-infrared (NIR) measurements since June 2018. In this paper, the major updates related to the production of the NIR PWV products of MERSI-II are discussed for the first time. In addition, the water vapor retrieval algorithm based on the MERSI-II NIR channels is introduced and derivations are made over clear land areas, clouds, and sun-glint areas over the ocean. Finally, the status and samples of the MERSI-II PWV products are presented. The accuracy of MERSI-II PWV products is validated using ground-based GPS measurements. The results show that the accuracies of the water vapor products based on the updated MERSI-II instrument are significantly improved compared with those of MERSI, because MERSI-II provides a better channel setting and new calibration method. The root-mean-square error and relative bias of MERSI-II PWV products are typically 1.8–5.5 mm and −3.0% to −14.3%, respectively, and thus comparable with those of other global remote sensing products of the same type.
2021, 38(8): 1367-1378.
doi: 10.1007/s00376-020-0258-5
Abstract:
This paper describes three algorithms for retrieving precipitation over oceans from brightness temperatures (TBs) of the Micro-Wave Humidity Sounder-2 (MHWS-2) onboard Fengyun-3C (FY-3C). For algorithm development, scattering-induced TB depressions (ΔTBs) of MWHS-2 at channels between 89 and 190 GHz were collocated to rain rates derived from measurements of the Global Precipitation Measurement’s Dual-frequency Precipitation Radar (DPR) for the year 2017. ΔTBs were calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel. These ΔTBs were then related to rain rates from DPR using (1) multilinear regression (MLR); the other two algorithms, (2) range searches (RS) and (3) nearest neighbor searches (NNS), are based on k-dimensional trees. While all three algorithms produce instantaneous rain rates, the RS algorithm also provides the probability of precipitation and can be understood in a Bayesian framework. Different combinations of MWHS-2 channels were evaluated using MLR and results suggest that adding 118 GHz improves retrieval performance. The optimal combination of channels excludes high-peaking channels but includes 118 GHz channels peaking in the mid and high troposphere. MWHS-2 observations from another year were used for validation purposes. The annual mean 2.5° × 2.5° gridded rain rates from the three algorithms are consistent with those from the Global Precipitation Climatology Project (GPCP) and DPR. Their correlation coefficients with GPCP are 0.96 and their biases are less than 5%. The correlation coefficients with DPR are slightly lower and the maximum bias is ~8%, partly due to the lower sampling density of DPR compared to that of MWHS-2.
This paper describes three algorithms for retrieving precipitation over oceans from brightness temperatures (TBs) of the Micro-Wave Humidity Sounder-2 (MHWS-2) onboard Fengyun-3C (FY-3C). For algorithm development, scattering-induced TB depressions (ΔTBs) of MWHS-2 at channels between 89 and 190 GHz were collocated to rain rates derived from measurements of the Global Precipitation Measurement’s Dual-frequency Precipitation Radar (DPR) for the year 2017. ΔTBs were calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel. These ΔTBs were then related to rain rates from DPR using (1) multilinear regression (MLR); the other two algorithms, (2) range searches (RS) and (3) nearest neighbor searches (NNS), are based on k-dimensional trees. While all three algorithms produce instantaneous rain rates, the RS algorithm also provides the probability of precipitation and can be understood in a Bayesian framework. Different combinations of MWHS-2 channels were evaluated using MLR and results suggest that adding 118 GHz improves retrieval performance. The optimal combination of channels excludes high-peaking channels but includes 118 GHz channels peaking in the mid and high troposphere. MWHS-2 observations from another year were used for validation purposes. The annual mean 2.5° × 2.5° gridded rain rates from the three algorithms are consistent with those from the Global Precipitation Climatology Project (GPCP) and DPR. Their correlation coefficients with GPCP are 0.96 and their biases are less than 5%. The correlation coefficients with DPR are slightly lower and the maximum bias is ~8%, partly due to the lower sampling density of DPR compared to that of MWHS-2.
2021, 38(8): 1379-1396.
doi: 10.1007/s00376-020-0010-1
Abstract:
This paper evaluates the microwave instruments onboard the latest Chinese polar-orbiting satellite, Fengyun 3D (FY-3D). Comparing three months of observations from the Microwave Temperature Sounder 2 (MWTS-2), the Microwave Humidity Sounder 2 (MWHS-2), and the Microwave Radiation Imager (MWRI) to Met Office short-range forecasts, we characterize the instrumental biases, show how those biases have changed with respect to their predecessors onboard FY-3C, and how they compare to the Advanced Technology Microwave Sounder (ATMS) onboard NOAA-20 and the Global Precipitation Measurement Microwave Imager (GMI). The MWTS-2 global bias is much reduced with respect to its predecessor and compares well to ATMS at equivalent channel frequencies, differing only by 0.36 ± 0.28 K (1σ) on average. A suboptimal averaging of raw digital counts is found to cause an increase in striping noise and an ascending—descending bias. MWHS-2 benefits from a new calibration method improving the 183-GHz humidity channels with respect to its predecessor and biases for these channels are within ± 1.9 K to ATMS. MWRI presents the largest improvements, with reduced global bias and standard deviation with respect to FY-3C; although, spurious, seemingly transient, brightness temperatures have been detected in the observations at 36.5 GHz (vertical polarization). The strong solar-dependent bias that affects the instrument on FY-3C has been reduced to less than 0.2 K on average for FY-3D MWRI. Experiments where radiances from these instruments were assimilated on top of a full global system demonstrated a neutral to positive impact on the forecasts, as well as on the fit to the background of independent instruments.
This paper evaluates the microwave instruments onboard the latest Chinese polar-orbiting satellite, Fengyun 3D (FY-3D). Comparing three months of observations from the Microwave Temperature Sounder 2 (MWTS-2), the Microwave Humidity Sounder 2 (MWHS-2), and the Microwave Radiation Imager (MWRI) to Met Office short-range forecasts, we characterize the instrumental biases, show how those biases have changed with respect to their predecessors onboard FY-3C, and how they compare to the Advanced Technology Microwave Sounder (ATMS) onboard NOAA-20 and the Global Precipitation Measurement Microwave Imager (GMI). The MWTS-2 global bias is much reduced with respect to its predecessor and compares well to ATMS at equivalent channel frequencies, differing only by 0.36 ± 0.28 K (1σ) on average. A suboptimal averaging of raw digital counts is found to cause an increase in striping noise and an ascending—descending bias. MWHS-2 benefits from a new calibration method improving the 183-GHz humidity channels with respect to its predecessor and biases for these channels are within ± 1.9 K to ATMS. MWRI presents the largest improvements, with reduced global bias and standard deviation with respect to FY-3C; although, spurious, seemingly transient, brightness temperatures have been detected in the observations at 36.5 GHz (vertical polarization). The strong solar-dependent bias that affects the instrument on FY-3C has been reduced to less than 0.2 K on average for FY-3D MWRI. Experiments where radiances from these instruments were assimilated on top of a full global system demonstrated a neutral to positive impact on the forecasts, as well as on the fit to the background of independent instruments.
2021, 38(8): 1397-1414.
doi: 10.1007/s00376-021-0401-y
Abstract:
All-sky (i.e., clear, cloudy, and precipitating conditions) assimilation of microwave observations shows potentially positive impacts on the improvement of the forecasts of cloud-associated weather processes. In this study, a typical mei-yu heavy precipitation event that occurred in 2017 was investigated, and the Weather Research and Forecasting data assimilation (WRFDA) as well as its 3D-Var assimilation scheme (excluding cloud and precipitation control variables) were applied to assimilate the Fengyun-3C (FY-3C) Microwave Humidity Sounder-2 (MWHS-2) observations under clear-sky (excluding the observations that are strongly affected by ice clouds and precipitation) and all-sky conditions. Three experiments including a control experiment without assimilating any observations, clear-sky, and all-sky experiments with only FY-3C/MWHS-2 observations assimilated were carried out. The results show that the all-sky assimilation approach that provides more cloud and precipitation information and increased more than 10% of the satellite data usage than the clear-sky experiment. Meanwhile, as compared with the control experiment, the all-sky assimilation reduced nearly 0.5% of the root mean square errors in the humidity fields, leading to more accurate forecast performances regarding the distribution and intensity of heavy rainfall; but it exhibited a neutral to negative impacts on the wind and temperature. Although the system used to conduct all-sky assimilation is only able to adjust control variables for moisture-, wind-, and temperature-related variables in the presence of cloud and does not benefit directly from cloud or precipitation information, the positive effects on heavy rainfall forecasts achieved in this study indicate a potential future benefit regarding disaster prevention and mitigation.
All-sky (i.e., clear, cloudy, and precipitating conditions) assimilation of microwave observations shows potentially positive impacts on the improvement of the forecasts of cloud-associated weather processes. In this study, a typical mei-yu heavy precipitation event that occurred in 2017 was investigated, and the Weather Research and Forecasting data assimilation (WRFDA) as well as its 3D-Var assimilation scheme (excluding cloud and precipitation control variables) were applied to assimilate the Fengyun-3C (FY-3C) Microwave Humidity Sounder-2 (MWHS-2) observations under clear-sky (excluding the observations that are strongly affected by ice clouds and precipitation) and all-sky conditions. Three experiments including a control experiment without assimilating any observations, clear-sky, and all-sky experiments with only FY-3C/MWHS-2 observations assimilated were carried out. The results show that the all-sky assimilation approach that provides more cloud and precipitation information and increased more than 10% of the satellite data usage than the clear-sky experiment. Meanwhile, as compared with the control experiment, the all-sky assimilation reduced nearly 0.5% of the root mean square errors in the humidity fields, leading to more accurate forecast performances regarding the distribution and intensity of heavy rainfall; but it exhibited a neutral to negative impacts on the wind and temperature. Although the system used to conduct all-sky assimilation is only able to adjust control variables for moisture-, wind-, and temperature-related variables in the presence of cloud and does not benefit directly from cloud or precipitation information, the positive effects on heavy rainfall forecasts achieved in this study indicate a potential future benefit regarding disaster prevention and mitigation.
2021, 38(8): 1415-1428.
doi: 10.1007/s00376-021-0326-5
Abstract:
MetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.
MetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.
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