Sub-daily Timescales Research Articles

Unraveling flood dynamics at sub-daily time scales in semi-arid to arid basins in south Morocco

Unraveling flood dynamics at sub-daily time scales in semi-arid to arid basins in south Morocco

How to use efficiently airborne criteria pollutants and radon-222 in source apportionment: A self-organizing maps approach

Pollutant source apportionment represents one of the fundamental activities in environmental science. Several efficient chemometric tools are available to the scope, mostly based on multivariate techniques and usually applied to aerosol chemical speciation data. In the present work, an alternative source profiling method is proposed, based on the self-organizing maps (SOM) algorithm. Moreover, the dataset used includes typical criteria pollutants and physical parameters related to airborne particulate matter widely used as a complement of aerosol source apportionment and largely available at a higher time resolution than bulk aerosol samplings, allowing the information on the dynamic behavior of the local airshed to be extended. In this work, data was collected at a coastal location in NW Italy, between January and July 2012. Hourly concentrations of typical gaseous pollutants (SO2, NO, NO2, benzene, toluene, (m-p)-xylene, o-xylene), black-carbon and particle number concentrations by an optical particle sizer (OPS) were collected. The dataset was integrated with radon-222 activity concentration and meteorological parameters to enrich and refine the information obtained by SOM computation as well as to improve the air pollution source localization. Despite the lower specificity of criteria pollutants, the approach developed was capable of revealing distinct pollution sources such as the urban background traffic, the coal-fired power plant active at the time of the study, and the harbor, in agreement with previous PM-based source apportionment studies carried out locally, while enlightening peculiar dynamical conditions detectable at the sub-daily time scale. The application of the SOM algorithm, with the integration of meteorological parameters and atmospheric radon, proved to be very efficient in unveiling the air pollution sources.

Evaluation of potential evapotranspiration models over fluxdata network cropland sites

Utilizing potential evapotranspiration (PET) for the estimation of actual evapotranspiration (AET) in cropland is highly valuable for determining agricultural water requirements and developing irrigation schedules. Discrepancies between anticipated and actual PET values may arise due to the limitations in previous reference standards employed for evaluation. A comprehensive evaluation of the accuracy of the PET models in cropland was conducted in this study. Non-water-stressed days with evaporation fraction (EF) exceeding the 95th percentile threshold were selected from cropland sites in the FLUXNET Tier 1 database as the basis for calibrating and validating 22 PET calculation models at the daily scale and 19 at the sub-daily scale. Results indicated that the Penman (1948), Penman (1963), and all radiation-based models showed strong performance, with R2, RMSE, and NSE of 0.82–0.89, 0.58–0.86 mm day−1, and 0.54–0.77 at the daily scale, and 0.73–0.81, 0.04–0.05 mm 0.5 h−1, and 0.62–0.72 at the sub-daily scale, respectively. Four recently developed radiation-based models have exhibited remarkable accuracy on a daily basis. The relative model errors (RMSE/MEAN) tended to decrease as net radiation (Rn), temperature (Ta), and vapor pressure deficit (VPD) increased at both daily and sub-daily time scales. The models displayed strong accuracy in estimating daily PET when Ta > 15 ℃ or 0.4 < VPD < 1.0, with RMSE/MEAN values ranging from 0.10 to 0.22 and R2 values ranging from 0.76 to 0.89. Higher accuracy in sub-daily scale PET estimates was achieved when Ta > 15 ℃ or 0.4 < VPD < 1.6, with RMSE/MEAN of 0.24–0.43 and R2 of 0.54–0.85, respectively. Additionally, the time lag between PET and Rn, Ta, and VPD increased as Rn, Ta, and VPD increased at the sub-daily scale, which may contribute to the reduced precision of sub-daily PET estimates compared to daily scale estimates. These findings suggest that PET models with calibrated parameters have the potential to serve as the basis for estimating cropland AET, and can provide direction for future model improvement.

Coupled model intercomparison project phase 6 (CMIP6) high resolution model intercomparison project (HighResMIP) bias in extreme rainfall drives underestimation of amazonian precipitation

Extreme rainfall events drive the amount and spatial distribution of rainfall in the Amazon and are a key driver of forest dynamics across the basin. This study investigates how the 3-hourly predictions in the High Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project, CMIP6) represent extreme rainfall events at annual, seasonal, and sub-daily time scales. TRMM 3B42 (Tropical Rainfall Measuring Mission) 3 h data were used as observations. Our results showed that eleven out of seventeen HighResMIP models showed the observed association between rainfall and number of extreme events at the annual and seasonal scales. Two models captured the spatial pattern of number of extreme events at the seasonal and annual scales better (higher correlation) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals. Our results suggest that higher model resolution is a crucial factor for capturing extreme rainfall events in the Amazon, but it might not be the sole factor. Improving the representation of Amazon extreme rainfall events in HighResMIP models can help reduce model rainfall biases and uncertainties and enable more reliable assessments of the water cycle and forest dynamics in the Amazon.

A web-based urban hydrology model for municipal scale applications

Extensive data and computational requirements limit the application of existing urban hydrology models at municipal scales. Community-enabled Lifecycle Analysis of Stormwater Infrastructure Costs (CLASIC) is a web-based deployment of the SWMM model with decoupled hydrologic and hydraulic components to enable hydrologic assessment at the municipal and larger scales. This study comprehensively evaluates the performance validity of CLASIC for characterization of hydrologic responses against SWMM and observed data. Furthermore, global sensitivity analysis is used to explore the significance of hydrologic and hydraulic model parameters across spatial and temporal scales. CLASIC reliably represents the urban hydrological processes and accurately quantifies stream discharge at the municipal scale and temporal scales greater than the catchment's time of concentration. Notably, the computational requirements of CLASIC are substantially lower than those of SWMM as the catchment drainage area increases. The application of CLASIC for flood assessment may be conducted with careful examination of the estimated peak discharge at sub-daily timescales.

Wind as a Driver of Peat CO2 Dynamics in a Northern Bog.

The online version contains supplementary material available at 10.1007/s10021-024-00904-1.

Observed Subdaily Variations in Air–Sea Turbulent Heat Fluxes under Different Marine Atmospheric Boundary Layer Stability Conditions in the Gulf Stream

Abstract Based on data collected from 14 buoys in the Gulf Stream, this study examines how hourly air–sea turbulent heat fluxes vary on subdaily time scales under different boundary layer stability conditions. The annual mean magnitudes of the subdaily variations in latent and sensible heat fluxes at all stations are 40 and 15 W m−2, respectively. Under near-neutral conditions, hourly fluctuations in air–sea humidity and temperature differences are the major drivers of subdaily variations in latent and sensible heat fluxes, respectively. When the boundary layer is stable, on the other hand, wind anomalies play a dominant role in shaping the subdaily variations in latent and sensible heat fluxes. In the context of a convectively unstable boundary layer, wind anomalies exert a strong controlling influence on subdaily variations in latent heat fluxes, whereas subdaily variations in sensible heat fluxes are equally determined by air–sea temperature difference and wind anomalies. The relative contributions by all physical quantities that affect subdaily variations in turbulent heat fluxes are further documented. For near-neutral and unstable boundary layers, the subdaily contributions are O(2) and O(1) W m−2 for latent and sensible heat fluxes, respectively, and they are less than O(1) W m−2 for turbulent heat fluxes under stable conditions. Significance Statement High-resolution buoy observations of air–sea variables in the Gulf Stream provide the opportunity to investigate the physical factors that determine subdaily variations in air–sea turbulent heat fluxes. This study addresses two key points. First, the observed subdaily amplitudes of heat fluxes are related to various processes, including wind fields and air–sea thermal effect differences. Second, the global sea surface heat budget is known to not be in near-zero balance and it ranges from several to tens of watts per square meter. Therefore, consideration of the relatively strong influence of subdaily variability in air–sea turbulent heat fluxes could provide a new strategy for solving the global heat budget balance problem.

Metabolic activity of Planktothrix rubescens and its consequences on oxygen dynamics in laboratory experiment: A stable isotope study.

Fluctuations in dissolved oxygen (DO) contents in natural waters can become intense during cyanobacteria blooms. In a reconnaissance study, we investigated DO concentrations and stable isotope dynamics during a laboratory experiment with the cyanobacterium Planktothrix rubescens in order to obtain insights into primary production under specific conditions. This observation was extended to sub-daily timescales with alternating light and dark phases. Dissolved oxygen concentrations and its isotopes (δ18ODO) ranged from 0.02 to 0.06 mmol · L-1 and from +9.6‰ to +23.4‰. The δ18ODO proved to be more sensitive than concentration measurements in response to metabolic variation and registered earlier shifts to dominance by respiration. Oxygen (O2) contents in the headspace and its isotopes (δ18OO2) ranged from 2.62 to 3.20 mmol · L-1 and from +9.8‰ to +21.9‰. Headspace samples showed less fluctuations in concentration and isotope trends because aquatic processes were hardly able to alter signals once the gas had reached the headspace. Headspace δ18OO2 values were corrected for gas-water equilibration and were determinedto be higher than the mean δ18OH2O of -8.7‰. This finding suggests that counteracting respiration was important even during the highest photosynthetic activity. Additionally, headspace analyses led to the definition of a fractionation factor for respiration (αR) of this cyanobacterium with a value of 0.980. This value confirms the one commonly used for cyanobacteria. Our findings may become important for the management of water bodies where decreases in DO are caused by cyanobacteria.

Sub-daily solar wind-magnetosphere energy transfer during major geomagnetic storms of solar cycle 23

The solar wind-magnetosphere energy transfer is one major way of quantifying the impacts of space weather on the Earth's environment. Studies on solar wind-magnetosphere energy transfer in terms of sub-daily time scales are quite sparse. Consequently, in order to investigate storm-time characteristics of the solar wind-magnetosphere energy transfer at a 4-hourly time scale over solar cycle 23, a total of 85 major geomagnetic storms (Dst ≤ −100 nT) were categorized into bins, using the points of minimum Dst values as reference points. Additionally, the monthly statistics of the storms occurrences over the solar cycle was carried out. The statistics followed a semi-annual periodicity, with a lesser peak in the months of May and August, and a major peak in October and November. Furthermore, the energy dissipated was estimated by the summation of aurora precipitation, Joule heating, and the ring current injection. Overall, from the data analyzed, we observed storm-time intensification of energy transfer. The magnetosphere received the highest energy during the time interval 0400–0800 UT (0900–1300 LT in the American sector, and most impactful there), while the lowest energy transfer was recorded between 0000 and 0400 UT (0200–0600 LT in the African/European sector, and least impactful there). We observed a synchrony between the energy dissipated and energy input for the storms’ main phases. Generally, storms driven by a composite of two Interplanetary Coronal Mass Ejections (ICMEs) transients transferred highest energy into the magnetosphere, while those driven by Co-rotating Interaction Regions (CIRs) transferred lowest energy.

Distinguishing the main climatic drivers to the variability of gross primary productivity at global FLUXNET sites

Climate exerts both short-term and long-term impacts on the ecosystem carbon assimilation. However, the main climatic drivers for the variability of gross primary productivity (GPP) remain unclear across various timescales and vegetation types. Here, we combine the state-of-the-art machine learning algorithms with a well-established explanatory method to explore the impacts of climatic factors on long-term GPP variability at global FLUXNET sites across four timescales and six plant functional types. Results show that diffuse shortwave radiation (SWdif) dominates GPP variability at the sub-daily (half-hourly to three hourly) timescales especially for the tree species, and acts as the secondary contributor after air temperature at the daily or longer timescales. Attribution analyses further showed that the main effects of SWdif are much higher than their interactive effects with other climatic factors in regulating the GPP variability. By identifying the main climatic drivers, this study improves the understanding of the climate-driven GPP variability and provides important implications for the future projection of ecosystem carbon assimilation under global climate change.

Ocean acidification enhances primary productivity and nocturnal carbonate dissolution in intertidal rock pools

Abstract. Human CO2 emissions are modifying ocean carbonate chemistry, causing ocean acidification and likely already impacting marine ecosystems. In particular, there is concern that coastal, benthic calcifying organisms will be negatively affected by ocean acidification, a hypothesis largely supported by laboratory studies. The inter-relationships between carbonate chemistry and marine calcifying communities in situ are complex, and natural mesocosms such as tidal pools can provide useful community-level insights. In this study, we manipulated the carbonate chemistry of intertidal pools to investigate the influence of future ocean acidification on net community production (NCP) and calcification (NCC) at emersion. Adding CO2 at the start of the tidal emersion to simulate future acidification (+1500 µatm pCO2, target pH 7.5) modified net production and calcification rates in the pools. By day, pools were fertilized by the increased CO2 (+20 % increase in NCP, from 10 to 12 mmol O2 m−2 h−1), while there was no measurable impact on NCC. During the night, pools experienced net community dissolution (NCC &lt; 0), even under present-day conditions, when waters were supersaturated with regard to aragonite. Adding CO2 to the pools increased nocturnal dissolution rates by 40 % (from −0.7 to −1.0 mmol CaCO3 m−2 h−1) with no consistent impact on nocturnal community respiration. Our results suggest that ocean acidification is likely to alter temperate intertidal community metabolism on sub-daily timescales, enhancing both diurnal community production and nocturnal calcium carbonate dissolution.

Beyond the Stokes approximation: shallow visco-elastic ice-sheet models

Abstract The hypothesis that ice-sheet evolution is only controlled by the long-term non-Newtonian viscous behavior of ice has been challenged by observations indicating that effects like brittle failure, stick-slip sliding, tides and wave action may affect ice-sheet evolution on sub-daily timescales. Over these timescales, the quasi-static-creep approximation is no longer appropriate and elastic effects become important. Simulating elastic effects in ice-sheet models over relevant timescales, however, remains challenging. Here, we show that by including a visco-elastic rheology and reintroducing the oft neglected acceleration term back into the ice-sheet stress balance, we can create a visco-elastic system where the velocity is locally determined and information propagates at the elastic wave speed. Crucially, the elastic wave speed can be treated like an adjustable parameter and set to any value to reproduce a range of phenomena, provided the wave speed is large compared to the viscous velocity. We illustrate the system using three examples. The first two examples demonstrate that the system converges to the steady-state viscous and elastic limits. The third example examines ice-shelf rifting and iceberg calving. This final example hints at the utility of the visco-elastic formulation in treating both long-term evolution and short-term environmental effects.

The KNMI Large Ensemble Time Slice (KNMI–LENTIS)

Abstract. Large-ensemble modelling has become an increasingly popular approach to studying the mean climate and the climate system’s internal variability in response to external forcing. Here we present the Royal Netherlands Meteorological Institute (KNMI) Large Ensemble Time Slice (KNMI–LENTIS): a new large ensemble produced with the re-tuned version of the global climate model EC-Earth3. The ensemble consists of two distinct time slices of 10 years each: a present-day time slice and a +2 K warmer future time slice relative to the present day. The initial conditions for the ensemble members are generated with a combination of micro- and macro-perturbations. The 10-year length of a single time slice is assumed to be too short to show a significant forced climate change signal, and the ensemble size of 1600 years (160 × 10 years) is assumed to be sufficient to sample the full distribution of climate variability. The time slice approach makes it possible to study extreme events on sub-daily timescales as well as events that span multiple years such as multi-year droughts and preconditioned compound events. KNMI–LENTIS is therefore uniquely suited to study internal variability and extreme events both at a given climate state and resulting from forced changes due to external radiative forcing. A unique feature of this ensemble is the high temporal output frequency of the surface water balance and surface energy balance variables, which are stored in 3-hourly intervals, allowing for detailed studies into extreme events. The large ensemble is particularly geared towards research in the land–atmosphere domain. EC-Earth3 has a considerable warm bias in the Southern Ocean and over Antarctica. Hence, users of KNMI–LENTIS are advised to make in-depth comparisons with observational or reanalysis data, especially if their studies focus on ocean processes, on locations in the Southern Hemisphere, or on teleconnections involving both hemispheres. In this paper, we will give some examples to demonstrate the added value of KNMI–LENTIS for extreme- and compound-event research and for climate-impact modelling.

Subdaily Slow Fault Slip Dynamics Captured by Low‐Frequency Earthquakes

AbstractGeodetic positioning is the geophysical record of reference for slow slip events, but typical daily solutions limit studies of the evolution of slow slip to its long‐term dynamics. Accompanying seismic low‐frequency earthquakes located precisely in time and space provide an opportunity to image slow slip dynamics at subdaily time scales. Here we show that a high‐resolution time history of low‐frequency earthquake fault slip alone can reproduce the geodetic record of slow slip that we observe to be dominated by subdaily fault slip dynamics. However, a simple linear model cannot accommodate the complex dynamics present throughout the slow slip cycle, and an analysis of different phases of the slow slip cycle shows that the ratio of geodetic to seismic fault slip varies as a function of time. This suggests that the low‐frequency earthquake source region saturates as slow slip grows in moment and area. We propose that rheological heterogeneities at the plate boundary associated with low‐frequency earthquakes do not play a significant role in the slow slip rupture process, thus implying that their activity is incidental to the driving aseismic slip.

“A comprehensive assessment of suitability of Global Precipitation Products for hydro-meteorological applications in a data-sparse Himalayan region”

Observation rainfall networks in developing countries such as Nepal face many challenges (like availability and quality). Global Precipitation Products (GPPs) could be an alternative to gauge-based observed rainfall (GOR) in hydro-meteorological studies. However, GPPs performance across the Himalayan regions is still unknown and is influenced by several factors such as spatial and temporal resolutions, primary data sources, etc. We have comprehensively assessed the suitability of the latest GPPs using categorical and continuous variable performance metrics for the Gandak River Basin in the Nepalese Himalayas. We then ranked GPPs for the first time using the Multicriteria Decision-Making technique. 11 out of 12 GPPs considered underestimated the annual rainfall in the basin. Performance of GPPs was also inconsistent for monthly/annual and daily timescales. At longer timescales, CHIRPS and IMERG_Final are better at representing the spatial and temporal pattern of the rainfall (spatial correlation of 0.75) and least percentage bias (PBIAS < 12%). At a daily timescale, IMERG_Final, ERA5, and PERSIANN_CDR stand out for probability of detection (POD) of rainfall, while all GPPs perform poorly in false alarm ratio (FAR). Although all GPPs have relatively high RMSE (6-14 mm/day), correlation (CC) with observed rainfall was high for IMERG_Final, ERA5, and MERRA_2 in most of the sub-basins. With elevation, the performance of all GPPs is reduced, as evidenced by higher PBIAS, and lower CC. Although there is plenty of room for improvements in rainfall estimation by GPPs, among the existing dataset, IMERG_Final scored best in the majority of the performance indicators and ranked first in five out of six sub-basins. It would be relatively the better choice in the data sparse Himalayan region when daily rainfall data is required. For applications that require monthly/annual rainfall, both CHIRPS and IMERG_Final are equally suitable. The method proposed in the study for assessing GPPs can be readily applied in other river basins and at sub-daily timescales.

The Potential of Deep Learning for Satellite Rainfall Detection over Data-Scarce Regions, the West African Savanna

Food and economic security in West Africa rely heavily on rainfed agriculture and are threatened by climate change and demographic growth. Accurate rainfall information is therefore crucial to tackling these challenges. Particularly, information about the occurrence and length of droughts as well as the onset date of the rainy season is essential for agricultural planning. However, existing rainfall models fail to accurately represent the highly variable and sparsely monitored West African rainfall patterns. In this paper, we show the potential of deep learning (DL) to model rainfall in the region and propose a methodology to develop DL models in data-scarce areas. We built two DL models for satellite rainfall (rain/no-rain) detection over northern Ghana from Meteosat TIR data based on standard DL architectures: Convolutional neural networks (CNNs) and convolutional long short-term memory neural networks (ConvLSTM). The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) and Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System (PERSIANN-CCS) products are used as benchmarks. We use rain gauge data from the Trans-African Hydro-Meteorological Observatory (TAHMO) for model development and performance evaluation. We show that our models compare well against existing products despite being considerably simpler, developed with a small training dataset—i.e., 8 stations covering 2.5 years with 20.4% of the data missing—and using TIR data alone. Concretely, our models consistently outperform PERSIANN-CCS for rain/no-rain detection at a sub-daily timescale. While IMERG is the overall best performer, the DL models perform better in the second half of the rainy season despite their simplicity (i.e., up to 120 k parameters). Our results suggest that DL-based regional models are a promising alternative to state-of-the-art global products for providing regional rainfall information, especially in meteorologically complex regions such as the (sub)tropics, which are poorly covered by ground-based rainfall observations.

Unfolding unique features of precipitation-temperature scaling across India

Warming of the climate is now unequivocal. However, probable dynamics of changes in the precipitation regimes remains disputable under potential future warming. Theoretically, increasing atmospheric heat translates to increase the atmospheric moisture holding capacity at the Clausius-Clapeyron (C-C) scaling rate in the absence of moisture limitation, which in turn raises the possibilities of more intense precipitation events. Here we investigated the scaling relationship between observed and reanalysis precipitation with surface air temperature and dew point temperature at daily and sub-daily time scales on annual and season-wise basis. To reach a robust conclusion, entire Indian mainland is considered as the study area that spans across a vast climatic characteristics. More influential temperature dependence is found for heavier precipitation events than the lighter ones on annual basis as well as for different seasons. Negative and peak type scaling patterns between the daily surface air temperature (SAT) and precipitation is noticed across the different climatic regimes of the study region. In contrast, positive dependence between dew point temperature (DPT) and precipitation is obtained to be dominant across the study region. Negative sensitivity noticed for the daily surface temperature is mainly the consequence of the local cooling effect due to the occurrence of precipitation and the moisture limitation at higher end of the local temperature. In general, the scaling relation varies from season to season, which tend towards a negative scaling in hotter seasons, but a positive scaling in colder seasons. Extreme precipitation can be better portrayed by sub-daily temperature conditions prior to the start of the storm events. Scaling relationship obtained between the sub-daily precipitation and surface air and dew point temperature exhibits greater consistency in the relationship across the regions. The Western Ghats region is consistently noticed to exhibit a positive scaling relation, whereas rest of the country exhibits mostly a peak type relation. The SAT considered up to 6-h prior to the precipitation might not reflect the actual response of precipitation towards the temperature increase, possibly due to the prevailing cloudy conditions preceding the precipitation event. Findings of this study provides a better understanding related to the anticipated changes in precipitation regimes under future climate with a warming condition across different climatic regimes.

Optimal Temporal Filtering of the Cosmic-Ray Neutron Signal to Reduce Soil Moisture Uncertainty

Cosmic ray neutron sensors (CRNS) are increasingly used to determine field-scale soil moisture (SM). Uncertainty of the CRNS-derived soil moisture strongly depends on the CRNS count rate subject to Poisson distribution. State-of-the-art CRNS signal processing averages neutron counts over many hours, thereby accounting for soil moisture temporal dynamics at the daily but not sub-daily time scale. This study demonstrates CRNS signal processing methods to improve the temporal accuracy of the signal in order to observe sub-daily changes in soil moisture and improve the signal-to-noise ratio overall. In particular, this study investigates the effectiveness of the Moving Average (MA), Median filter (MF), Savitzky-Golay (SG) filter, and Kalman filter (KF) to reduce neutron count error while ensuring that the temporal SM dynamics are as good as possible. The study uses synthetic data from four stations for measuring forest ecosystem-atmosphere relations in Africa (Gorigo) and Europe (SMEAR II (Station for Measuring Forest Ecosystem-Atmosphere Relations), Rollesbroich, and Conde) with different soil properties, land cover and climate. The results showed that smaller window sizes (12 h) for MA, MF and SG captured sharp changes closely. Longer window sizes were more beneficial in the case of moderate soil moisture variations during long time periods. For MA, MF and SG, optimal window sizes were identified and varied by count rate and climate, i.e., estimated temporal soil moisture dynamics by providing a compromise between monitoring sharp changes and reducing the effects of outliers. The optimal window for these filters and the Kalman filter always outperformed the standard procedure of simple 24-h averaging. The Kalman filter showed its highest robustness in uncertainty reduction at three different locations, and it maintained relevant sharp changes in the neutron counts without the need to identify the optimal window size. Importantly, standard corrections of CRNS before filtering improved soil moisture accuracy for all filters. We anticipate the improved signal-to-noise ratio to benefit CRNS applications such as detection of rain events at sub-daily resolution, provision of SM at the exact time of a satellite overpass, and irrigation applications.

Statistical uncertainty analysis-based precipitation merging (SUPER): A new framework for improved global precipitation estimation

Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. Here, a Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER) is proposed for addressing these challenges. Specifically, a quadruple collocation analysis is employed to estimate precipitation error variances and covariances for commonly used precipitation products. These error estimates are subsequently used for merging all products via a least-squares minimization approach. In addition, false-alarm precipitation events are removed via a reference rain/no-rain time series estimated by a newly developed categorical variable merging method. As such, SUPER does not require any rain gauge observations to reduce daily random and rain/no-rain classification errors. Additionally, by considering precipitation product inter-dependency, SUPER avoids overfitting measurement noise present in multi-source precipitation products. Results show that the overall RMSE of SUPER-based precipitation is 3.35 mm/day and the daily correlation with gauge observations is 0.71 [−] – metrics that are generally superior to recent precipitation reanalyses and remote sensing products. In this way, we seek to propose a new framework for robustly generating global precipitation datasets that can improve land surface and hydrological modeling skill in data-sparse regions.

Benchmarking Simulated Precipitation Variability Amplitude across Time Scales

Abstract Objective performance metrics that measure precipitation variability across time scales from subdaily to interannual are presented and applied to Historical simulations of Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. Three satellite-based precipitation estimates (IMERG, TRMM, and CMORPH) are used as reference data. We apply two independent methods to estimate temporal variability of precipitation and compare the consistency in their results. The first method is derived from power spectra analysis of 3-hourly precipitation, measuring forced variability by solar insolation (diurnal and annual cycles) and internal variability at different time scales (subdaily, synoptic, subseasonal, seasonal, and interannual). The second method is based on time averaging and facilitates estimating the seasonality of subdaily variability. Supporting the robustness of our metric, we find a near equivalence between the results obtained from the two methods when examining simulated-to-observed ratios over large domains (global, tropics, extratropics, land, or ocean). Additionally, we demonstrate that our model evaluation is not very sensitive to the discrepancies between observations. Our results reveal that CMIP5 and CMIP6 models in general overestimate the forced variability while they underestimate the internal variability, especially in the tropical ocean and higher-frequency variability. The underestimation of subdaily variability is consistent across different seasons. The internal variability is overall improved in CMIP6, but remains underestimated, and there is little evidence of improvement in forced variability. Increased horizontal resolution results in some improvement of internal variability at subdaily and synoptic time scales, but not at longer time scales.