scholarly journals Evaluation of Long-Term SSM/I-Based Precipitation Records over Land

2014 ◽  
Vol 15 (5) ◽  
pp. 2012-2029 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Dara Entekhabi ◽  
Dennis B. McLaughlin
Keyword(s):  
Soil Moisture ◽  
Stage Iv ◽  
Precipitation Intensity ◽  
Position Errors ◽  
Retrieval Algorithms ◽  
Data Refresh ◽  
Microwave Imager ◽  
Precipitation Records ◽  
Global Precipitation

Abstract The record of global precipitation mapping using Special Sensor Microwave Imager (SSM/I) measurements now extends over two decades. Similar measurements, albeit with different retrieval algorithms, are to be used in the Global Precipitation Measurement (GPM) mission as part of a constellation to map global precipitation with a more frequent data refresh rate. Remotely sensed precipitation retrievals are prone to both magnitude (precipitation intensity) and phase (position) errors. In this study, the ground-based radar precipitation product from the Next Generation Weather Radar stage-IV (NEXRAD-IV) product is used to evaluate a new metric of error in the long-term SSM/I-based precipitation records. The new metric quantifies the proximity of two multidimensional datasets. Evaluation of the metric across the years shows marked seasonality and precipitation intensity dependence. Drifts and changes in the instrument suite are also evident. Additionally, the precipitation retrieval errors conditional on an estimate of background surface soil moisture are estimated. The dynamic soil moisture can produce temporal variability in surface emissivity, which is a source of error in retrievals. Proper filtering has been applied in the analysis to differentiate between the detection error and the retrieval error. The identification of the different types of errors and their dependence on season, intensity, instrument, and surface conditions provide guidance to the development of improved retrieval algorithms for use in GPM constellation-based precipitation data products.

Bias Correction of Gauge Data and its Effect on Precipitation Climatology over Mainland China

2019 ◽  
Vol 58 (10) ◽  
pp. 2177-2196 ◽  
Author(s):  
Yingxian Zhang ◽  
Yuyu Ren ◽  
Guoyu Ren ◽  
Guofu Wang
Keyword(s):  
Bias Correction ◽  
Arid Regions ◽  
Mainland China ◽  
Rain Gauge ◽  
Precipitation Intensity ◽  
Annual Precipitation ◽  
National Network ◽  
The Past ◽  
Precipitation Records

AbstractTypical rain gauge measurements have long been recognized to underestimate actual precipitation. Long-term daily precipitation records during 1961–2013 from a dense national network of 2379 gauges were corrected to remove systematic errors caused by trace precipitation, wetting losses, and wind-induced undercatch. The corrected percentage was higher in cold seasons and lower in warm seasons. Both trace precipitation and wetting loss corrections were more important in arid regions than in wet regions. A greater correction percentage for wind-induced error could be found in cold and arid regions, as well as high wind speed areas. Generally, the annual precipitation amounts as well as the annual precipitation intensity increased to varying degrees after bias correction with the maximum percentage being about 35%. More importantly, the bias-corrected snowfall amount as well as the rainstorm amount increased remarkably by percentages of more than 50% and 18%, respectively. Remarkably, the total number of actual rainstorm events during the past 53 years could be 90 days more than the observed rainstorm events in some coastal areas of China. Therefore, the actual amounts of precipitation, snowfall, and intense rainfall were much higher than previously measured over China. Bias correction is thus needed to obtain accurate estimates of precipitation amounts and precipitation intensity.

scholarly journals DEVELOPMENT OF A SPECIAL SENSOR MICROWAVE IMAGER (SSM/I) ALGORITHM FOR LONG-TERM MONITORING OF SOIL MOISTURE

2007 ◽  
Vol 51 ◽  
pp. 205-210
Author(s):  
Tetsu OHTA ◽  
Toshio KOIKE ◽  
Hui LU ◽  
David N. KURIA ◽  
Hiroyuki TSUTSUI ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Long Term Monitoring ◽  
Microwave Imager ◽  
Term Monitoring

scholarly journals Approximating Long-Term Statistics Early in the Global Precipitation Measurement Era

2017 ◽  
Vol 21 (3) ◽  
pp. 1-10 ◽  
Author(s):  
Thomas Stanley ◽  
Dalia B. Kirschbaum ◽  
George J. Huffman ◽  
Robert F. Adler
Keyword(s):  
Weather Prediction ◽  
Tropical Rainfall Measuring Mission ◽  
Quantile Mapping ◽  
Empirical Distributions ◽  
Precipitation Measurement ◽  
Crop Forecasting ◽  
Precipitation Records ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMM’s successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities, such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.

scholarly journals SM2RAIN-ASCAT (2007–2018): global daily satellite rainfall from ASCAT soil moisture

2019 ◽  
Author(s):  
Luca Brocca ◽  
Paolo Filippucci ◽  
Sebastian Hahn ◽  
Luca Ciabatta ◽  
Christian Massari ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Regional Scale ◽  
Global Scale ◽  
Time Inconsistency ◽  
Rainfall Data ◽  
Uniform Density ◽  
Data Record ◽  
Polar Orbiting Satellite ◽  
Global Precipitation

Abstract. Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products obtained from rain gauges, remote sensing and meteorological modelling suffer from space and time inconsistency due to non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent bottom up approach that uses satellite soil moisture observations for estimating rainfall through the SM2RAIN algorithm is suited to build long-term and consistent rainfall data record as a single polar orbiting satellite sensor is used. We exploit here the Advanced SCATterometer (ASCAT) on board three Metop satellites, launched in 2006, 2012 and 2018. The continuity of the scatterometer sensor on European operational weather satellites is ensured until mid-2040s through the Metop Second Generation Programme. By applying SM2RAIN algorithm to ASCAT soil moisture observations a long-term rainfall data record can be obtained, also operationally available in near real time. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN-ASCAT global daily rainfall dataset at 12.5 km sampling (2007–2018). The quality of SM2RAIN-ASCAT dataset is assessed on a regional scale through the comparison with high-quality ground networks in Europe, United States, India and Australia. Moreover, an assessment on a global scale is provided by using the Triple Collocation technique allowing us also the comparison with other global products such as the latest European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), the Global Precipitation Measurement (GPM) mission, and the gauge-based Global Precipitation Climatology Centre (GPCC) product. Results show that the SM2RAIN-ASCAT rainfall dataset performs relatively well both at regional and global scale, mainly in terms of root mean square error when compared to other datasets. Specifically, SM2RAIN-ASCAT dataset provides better performance better than GPM and GPCC in the data scarce regions of the world, such as Africa and South America. In these areas we expect the larger benefits in using SM2RAIN-ASCAT for hydrological and agricultural applications.The SM2RAIN-ASCAT dataset is freely available at https://doi.org/10.5281/zenodo.2591215.

scholarly journals Prototyping a Generic, Unified Land Surface Classification and Screening Methodology for GPM-Era Microwave Land Precipitation Retrieval Algorithms

2011 ◽  
Vol 50 (6) ◽  
pp. 1200-1211 ◽  
Author(s):  
Arief Sudradjat ◽  
Nai-Yu Wang ◽  
Kaushik Gopalan ◽  
Ralph R. Ferraro
Keyword(s):  
Land Surface ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Ancillary Data ◽  
Surface Cover ◽  
Cover Type ◽  
Retrieval Algorithms ◽  
Surface Classification ◽  
Microwave Imager ◽  
Global Precipitation

AbstractA prototype generic, unified land surface classification and screening methodology for Global Precipitation Measurement (GPM)-era microwave land precipitation retrieval algorithms by using ancillary datasets is developed. As an alternative to the current radiometer-determined approach, the new methodology is shown to be promising in improving rain detection by providing better surface-cover-type information. The early prototype new surface screening scheme was applied to the current version of the Goddard profiling algorithm that is used for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (GPROFV6). It has shown improvements in surface-cover-type classification and hence better precipitation retrieval comparisons with TRMM precipitation radar level-2 (L2) (2A25) data and the Global Precipitation Climatology Project (GPCP) version-2.1 (GPCPV2.1) datasets. The new ancillary data approach removes the current dependency of the screening step on relatively different satellite-specific channels and ensures the comparability and continuity of satellite-based precipitation products from different platforms. This is particularly important for advancing the current state of precipitation retrieval over land and for use in merged rainfall products.

scholarly journals SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations

2019 ◽  
Vol 11 (4) ◽  
pp. 1583-1601 ◽  
Author(s):  
Luca Brocca ◽  
Paolo Filippucci ◽  
Sebastian Hahn ◽  
Luca Ciabatta ◽  
Christian Massari ◽  
...  
Keyword(s):  
Soil Moisture ◽  
The United States ◽  
Global Scale ◽  
Time Inconsistency ◽  
Rainfall Data ◽  
Uniform Density ◽  
Rainfall Events ◽  
Data Record ◽  
Global Precipitation

Abstract. Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent “bottom-up” approach that exploits satellite soil moisture observations for estimating rainfall through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data record as a single polar orbiting satellite sensor is used. Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp) satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System programme. The continuity of the scatterometer sensor is ensured until the mid-2040s through the MetOp Second Generation Programme. Therefore, by applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a 12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record is assessed on a regional scale through comparison with high-quality ground networks in Europe, the United States, India, and Australia. Moreover, an assessment on a global scale is provided by using the triple-collocation (TC) technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the gauge-based Global Precipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relatively well at both a regional and global scale, mainly in terms of root mean square error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data record provides performance better than IMERG and GPCC in data-scarce regions of the world, such as Africa and South America. In these areas, we expect larger benefits in using SM2RAIN–ASCAT for hydrological and agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist of the underestimation of peak rainfall events and the presence of spurious rainfall events due to high-frequency soil moisture fluctuations that might be corrected in the future with more advanced bias correction techniques. The SM2RAIN–ASCAT data record is freely available at https://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end of August 2019).

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Laparoscopic Revisional Surgery After Failed Heller Myotomy for Esophageal Achalasia: Long-Term Outcome at a Single Tertiary Center

2021 ◽  
Author(s):  
Giovanni Capovilla ◽  
Renato Salvador ◽  
Luca Provenzano ◽  
Michele Valmasoni ◽  
Lucia Moletta ◽  
...  
Keyword(s):  
Heller Myotomy ◽  
Revisional Surgery ◽  
Stage Iv ◽  
Postoperative Outcomes ◽  
Control Group ◽  
Term Outcome ◽  
Long Term Outcome ◽  
Eckardt Score ◽  
Stage Iv Disease

Abstract Background Laparoscopic Heller myotomy (HM) has gained acceptance as the gold standard of treatment for achalasia. However, 10–20% of the patients will experience symptom recurrence, thus requiring further treatment including pneumodilations (PD) or revisional surgery. The aim of our study was to assess the long-term outcome of laparoscopic redo HM. Methods Patients who underwent redo HM at our center between 2000 and 2019 were enrolled. Postoperative outcomes of redo HM patients (redo group) were compared with that of patients who underwent primary laparoscopic HM in the same time span (control group). For the control group, we randomly selected patients matched for age, sex, FU time, Eckardt score (ES), previous PD, and radiological stage. Failure was defined as an Eckardt score > 3 or the need for re-treatment. Results Forty-nine patients underwent laparoscopic redo HM after failed primary HM. A new myotomy on the right lateral wall of the EGJ was the procedure of choice in the majority of patients (83.7%). In 36 patients (73.5%) an anti-reflux procedure was deemed necessary. Postoperative outcomes were somewhat less satisfactory, albeit comparable to the control group; the incidence of postoperative GERD was higher in the redo group (p < 0.01). At a median 5-year FU time, a good outcome was obtained in 71.4% of patients in the redo group; further 5 patients (10.2%) obtained a long-term symptom control after complementary PD, thus bringing the overall success rate to 81.6%. Stage IV disease at presentation was independently associated with a poor outcome of revisional LHD (p = 0.003). Conclusions This study reports the largest case series of laparoscopic redo HM to date. The procedure, albeit difficult, is safe and effective in relieving symptoms in this group of patients with a highly refractory disease. The failure rate, albeit not significantly, and the post-operative reflux are higher than after primary HM. Patients with stage IV disease are at high risk of esophagectomy.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

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